STD/C-Filtered, Truncated Taylor Family FIR Filter [Loxx]STD/C-Filtered, Truncated Taylor Family FIR Filter is a FIR Digital Filter that uses Truncated Taylor Family of Windows. Taylor functions are obtained by adding a weighted-cosine series to a constant (called a pedestal). A simpler form of these functions can be obtained by dropping some of the higher-order terms in the Taylor series expansion. If all other terms, except for the first two significant ones, are dropped, a truncated Taylor function is obtained. This is a generalized window that is expressed as:
(1 + K) / 2 + (1 - K) / 2 * math.cos(2.0 * math.pi *n / N) where 0 ≤ |n| ≤ N/2
Here k can take the values in the range 0≤k≤1. We note that the Hann 0 ≤ |n| ≤ window is a special case of the truncated Taylor family with k = 0 and Rectangular 0 ≤ |n| ≤ window (SMA) is a special case of the truncated Taylor family with k = 1.
Truncated Taylor Family of Windows amplitudes for this indicator with K = 0.5
This indicator also includes Standard Deviation and Clutter filtering.
What is a Standard Devaition Filter?
If price or output or both don't move more than the (standard deviation) * multiplier then the trend stays the previous bar trend. This will appear on the chart as "stepping" of the moving average line. This works similar to Super Trend or Parabolic SAR but is a more naive technique of filtering.
What is a Clutter Filter?
For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal.
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
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energies_correlation_zscoreA table to help track correlations between the four major energies contracts of the CME. The table shows the z-score of the current correlation value between HO, RB, CL, and NG. The inputs are:
- timeframe: the timeframe of the calcluation. the default is 5 minutes.
- window: the rolling window over which to calculate the correlations. the default is 48, or four hours given the default timeframe.
A score of zer means that the correlation over the latest window is in line with the average for all windows sampled from the chart history. More positive scores imply higher positive correlation than normal, and vice versa for negative scores.
Cumulative Returns by Session [BackQuant]Cumulative Returns by Session
What this is
This tool breaks the trading day into three user-defined sessions and tracks how much each session contributes to return, volatility, and volume. It then aggregates results over a rolling window so you can see which session has been pulling its weight, how streaky each session has been, and how sessions relate to one another through a compact correlation heatmap.
We’ve also given the functionality for the user to use a simplified table, just by switching off all settings they are not interested in.
How it works
1) Session segmentation
You define APAC, EU, and US sessions with explicit hours and time zones. The script detects when each session starts and ends on every intraday bar and records its open, intraday high and low, close, and summed volume.
2) Per-session math
At each session end the script computes:
Return — either Percent: (Close−Open)÷Open×100(Close − Open) ÷ Open × 100(Close−Open)÷Open×100 or Points: (Close−Open)(Close − Open)(Close−Open), based on your selection.
Volatility — either Range: (High−Low)÷Open×100(High − Low) ÷ Open × 100(High−Low)÷Open×100 or ATR scaled by price: ATR÷Open×100ATR ÷ Open × 100ATR÷Open×100.
Volume — total volume transacted during that session.
3) Storage and lookback
Each day’s three session stats are stored as a row. You choose how many recent sessions to keep in memory. The script then:
Builds cumulative returns for APAC, EU, US across the lookback.
Computes averages, win rates, and a Sharpe-like ratio avgreturn÷avgvolatilityavg return ÷ avg volatilityavgreturn÷avgvolatility per session.
Tracks streaks of positive or negative sessions to show momentum.
Tracks drawdowns on cumulative returns to show worst runs from peak.
Computes rolling means over a short window for short-term drift.
4) Correlation heatmap
Using the stored arrays of session returns, the script calculates Pearson correlations between APAC–EU, APAC–US, and EU–US, and colors the matrix by strength and sign so you can spot coupling or decoupling at a glance.
What it plots
Three lines: cumulative return for APAC, EU, US over the chosen lookback.
Zero reference line for orientation.
A statistics table with cumulative %, average %, positive session rate, and optional columns for volatility, average volume, max drawdown, current streak, return-to-vol ratio, and rolling average.
A small correlation heatmap table showing APAC, EU, US cross-session correlations.
How to use it
Pick the asset — leave Custom Instrument empty to use the chart symbol, or point to another symbol for cross-asset studies.
Set your sessions and time zones — defaults approximate APAC, EU, and US hours, but you can align them to exchange times or your workflow.
Choose calculation modes — Percent vs Points for return, Range vs ATR for volatility. Points are convenient for futures and fixed-tick assets, Percent is comparable across symbols.
Decide the lookback — more sessions smooths lines and stats; fewer sessions makes the tool more reactive.
Toggle analytics — add volatility, volume, drawdown, streaks, Sharpe-like ratio, rolling averages, and the correlation table as needed.
Why session attribution helps
Different sessions are driven by different flows. Asia often sets the overnight tone, Europe adds liquidity and direction changes, and the US session can dominate range expansion. Separating contributions by session helps you:
Identify which session has been the main driver of net trend.
Measure whether volatility or volume is concentrated in a specific window.
See if one session’s gains are consistently given back in another.
Adapt tactics: fade during a mean-reverting session, press during a trending session.
Reading the tables
Cumulative % — sum of session returns over the lookback. The sign and slope tell you who is carrying the move.
Avg Return % and Positive Sessions % — direction and hit rate. A low average but high hit rate implies many small moves; the reverse implies occasional big swings.
Avg Volatility % — typical intrabars range for that session. Compare with Avg Return to judge efficiency.
Return/Vol Ratio — return per unit of volatility. Higher is better for stability.
Max Drawdown % — worst cumulative give-back within the lookback. A quick way to spot riskiness by session.
Current Streak — consecutive up or down sessions. Useful for mean-reversion or regime awareness.
Rolling Avg % — short-window drift indicator to catch recent turnarounds.
Correlation matrix — green clusters indicate sessions tending to move together; red indicates offsetting behavior.
Settings overview
Basic
Number of Sessions — how many recent days to include.
Custom Instrument — analyze another ticker while staying on your current chart.
Session Configuration and Times
Enable or hide APAC, EU, US rows.
Set hours per session and the specific time zone for each.
Calculation Methods
Return Calculation — Percent or Points.
Volatility Calculation — Range or ATR; ATR Length when applicable.
Advanced Analytics
Correlation, Drawdown, Momentum, Sharpe-like ratio, Rolling Statistics, Rolling Period.
Display Options and Colors
Show Statistics Table and its position.
Toggle columns for Volatility and Volume.
Pick individual colors for each session line and row accents.
Common applications
Session bias mapping — find which window tends to trend in your market and plan exposure accordingly.
Strategy scheduling — allocate attention or risk to the session with the best return-to-vol ratio.
News and macro awareness — see if correlation rises around central bank cycles or major data releases.
Cross-asset monitoring — set the Custom Instrument to a driver (index future, DXY, yields) to see if your symbol reacts in a particular session.
Notes
This indicator works on intraday charts, since sessions are defined within a day. If you change session clocks or time zones, give the script a few bars to accumulate fresh rows. Percent vs Points and Range vs ATR choices affect comparability across assets, so be consistent when comparing symbols.
Session context is one of the simplest ways to explain a messy tape. By separating the day into three windows and scoring each one on return, volatility, and consistency, this tool shows not just where price ended up but when and how it got there. Use the cumulative lines to spot the steady driver, read the table to judge quality and risk, and glance at the heatmap to learn whether the sessions are amplifying or canceling one another. Adjust the hours to your market and let the data tell you which session deserves your focus.
FibNexus [CHE]FibNexus — Auto-Fibonacci with Adaptive TrendLen + TFRSI Triggers
What it is.
FibNexus is a chart overlay that auto-anchors Fibonacci levels to the most relevant swing range without any manual timeframe picking. It does this by computing an adaptive trend length (“TrendLen”) from recent price behavior, then drawing retracements/extensions from the detected swing High/Low. A built-in TFRSI module adds LONG/SHORT triggers and ready-made alerts.
What makes FibNexus different (the TrendLen edge)
Most Fibonacci tools either (a) use fixed lookbacks or (b) force you to choose a higher reference timeframe (or a multiplier of it) and then place Fibs on those higher-TF swings. Your earlier Ultimate Fibonacci Trading Tool \ follows that higher-reference approach (auto TF, multiplier, or manual) and emphasizes custom level/label options. ( )
FibNexus flips that workflow:
* It doesn’t rely on a higher timeframe or a static lookback.
* Instead, it measures multiple window lengths inside the current chart timeframe and selects the one that best fits the data right now.
* From that data-driven window, it automatically finds the most recent swing high & low and draws the entire Fib stack from there.
* When the statistically “best” window changes, anchors update once, labels refresh cleanly, and then lines just extend to the right on each new bar.
Result: No more guesswork about “which timeframe or lookback should I use?”—FibNexus adapts the anchors to market conditions and keeps the drawing noise low.
How TrendLen works (transparent, deterministic)
1. Scan windows: The script evaluates a series of lookbacks (10, 20, …, 500 bars).
2. Score by correlation: For each window, it computes the correlation between price and its lagged version and picks the window with the highest correlation (the strongest, most self-consistent trend segment).
3. Anchor the swing: On a confirmed bar and only when TrendLen changes, it scans the last `TrendLen` bars to capture the highest high and lowest low and marks them with “X”.
4. Draw once, extend later: It deletes the old Fib objects, redraws the active levels from those anchors, and from then on extends the lines to the right as new bars print (no redraw spam).
This makes FibNexus responsive (it adapts when the structure shifts) and quiet (it doesn’t constantly repaint Fibs).
Fibonacci engine (levels, labels, direction)
* Retracements: 0.000 · 0.236 · 0.382 · 0.500 · 0.618 · 0.786 · 1.000
* Extensions: 1.618 · 2.618 · 3.618 · 4.236
* Label styles: *Default* (percent + price), *None*, *Percentage*, *Price*
* Label sizing: *tiny → huge*
* Bull/Bear context: Direction is inferred from mid-range positioning; prices are projected accordingly (retracement vs. extension math is handled for both cases).
* Selective toggles: You can show/hide any level and color it independently.
Momentum & signals (TFRSI module)
FibNexus embeds your TFRSI (“The Forbidden RSI \ ”) as the momentum/trigger layer. TFRSI is your open-source oscillator published on TradingView and designed for fast, normalized momentum readouts with customizable length/smoothing. ( )
* Defaults: `TFRSI length = 6`, `signal smoothing = 2`
* Triggers:
* LONG when TFRSI crosses up through the Long level (default 2.0)
* SHORT when TFRSI crosses down through the Short level (default 98.0)
* On-chart labels: Green LONG under the bar, red SHORT above the bar.
* Spam control: Keep only the N most recent labels to avoid clutter.
* Confirmed bars only: Signals/labels finalize at bar close to reduce flicker.
Alerts (ready for TradingView)
* LONG signal (TFRSI crossover)
* SHORT signal (TFRSI crossunder)
* TrendLen changed (anchors/Fibs recalculated)
* Price crossed a Fib level (any active level)
Use the provided `alertcondition(...)` entries in the TV dialog. Optionally enable instant `alert()` calls with verbose text (avoid duplicates if you also add alertconditions).
Typical use-cases & playbook
* Level reaction trading: In trends, watch 0.382 / 0.5 / 0.618 for reaction. A TFRSI up-cross near a retracement in an uptrend is a straightforward continuation setup; the opposite applies in downtrends.
* Breakout objectives: After clearing the 1.000 line (old swing), 1.618 is a common first extension target; beyond that, 2.618/3.618/4.236 map stretch objectives.
* Chop control: In range conditions, keep signals conservative (e.g., stick with the tight defaults 2.0/98.0 or raise thresholds). Always seek confluence (candlesticks, volume, HTF bias).
* Less micromanagement: You don’t need to babysit timeframe selection or anchors—TrendLen recomputes only when the data say so.
Inputs (by group)
* Core: TFRSI length & smoothing.
* Fibonacci Levels: Per-level toggles, numeric values, colors.
* Fibonacci Labels: Style (percentage/price/both/none) and size.
* Signals: Max number of visible LONG/SHORT labels (or 0 = off).
* TFRSI Trigger: Long/Short thresholds (defaults 2.0 / 98.0).
* Alerts: Master enable, per-event toggles, optional instant `alert()`.
Performance & UX
* Overlay indicator; efficient object handling.
* Clean redraw policy: Full re-draw only when TrendLen changes; otherwise Fibs extend horizontally.
* Clarity: Auto-marked swing anchors (“X”), configurable labels/colors.
Credits & references
* TFRSI – “The Forbidden RSI \ ” (open-source publication and description on TradingView). Used here as the momentum basis.
* “Ultimate Fibonacci Trading Tool \ ” (your earlier open-source tool on TradingView). Focuses on higher-reference timeframe selection (auto/multiplier/manual) and rich labeling controls; FibNexus replaces the fixed/higher-TF anchor logic with adaptive TrendLen in the current timeframe.
Risk disclaimer
This indicator is for educational/information purposes only and is not financial advice. No performance guarantees; past behavior does not predict future results. Trading involves substantial risk (including total loss). Always do your own research, test on demo, use risk management, and consult a licensed advisor where appropriate. Use at your own risk.
Disclaimer:
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Enhance your trading precision and confidence with FibNexus ! 🚀
Happy trading
Chervolino
FUMO 200 MagnetWhat it does
FUMO Magnet measures how far price has stretched away from its long-term “magnet” — a blended EMA/SMA moving average (200 by default).
It plots a logarithmic deviation (optionally normalized) as an oscillator around zero.
Above 0** → price is above the magnet (stretched up)
Below 0** → price is below the magnet (stretched down)
Guide levels** highlight potential overbought/oversold zones
---
Why log deviation?
Log returns make extremes comparable across cycles and compress exponential trends — especially useful for BTC and other crypto assets.
Normalization modes further adjust the scale, keeping the oscillator readable on any chart.
---
Inputs
**Base**
* Source (default: Close)
* Base Length (default: 200 EMA/SMA)
* EMA vs SMA weight (%) — 0% = pure SMA, 100% = pure EMA, 50% = blended
* EMA smoothing of deviation — acts as a noise filter
**Normalization**
* None (Log Deviation) — raw log stretch in % terms
* Z-score — deviation in standard deviations (σ)
* Robust Z (MAD) — deviation vs median absolute deviation, resistant to outliers
* Tanh squash — smooth nonlinear squash of extremes for compact scale
* Normalization window (for Z / MAD)
* Tanh scale (lower = stronger squash)
* Clamp after normalization — hard cap at ±X
**Levels**
* Guide levels (Upper / Lower) — visual thresholds (default ±12)
* Zero line toggle
---
### How to read it
* **Trend bias**: sustained time above 0 = uptrend, below 0 = downtrend
* **Stretch / mean reversion**: the farther from 0, the higher the reversion risk
* **Cross-checks**: combine with structure (HH/HL, LH/LL), volume, or momentum (RSI, MACD)
---
### Recommended settings by timeframe
**Long-term (1D / 1W)**
* Normalization: None (Log Deviation)
* Base Length: 200
* EMA vs SMA weight: 50% (adjust 35–65% for faster/slower magnet)
* Deviation smoothing: 20 (10–30 range)
* Guide levels: ±12 to ±20
* Use case: cycle extremes, portfolio rebalancing, trim/add logic
**Swing (4H – 1D)**
* Normalization: Z-score
* Window: 200 (100–250)
* Smoothing: 14–20
* Guide levels: ±2σ to ±3σ
* Use case: stretched conditions across regimes; ±3σ is rare, often mean-reverts
**Intraday / Active swing (1H – 4H)**
* Normalization: Robust Z (MAD)
* Window: 200 (150 for faster response)
* Smoothing: 10–16
* Guide levels: ±3 to ±4 (robust units)
* Use case: handles spikes better than σ, fewer false overbought/oversold signals
**Scalping / Universal readability (15m – 1H)**
* Normalization: Tanh squash
* Tanh scale: 6–10 (start with 8)
* Smoothing: 8–12
* Guide levels: ±8 to ±12
* Use case: compact panel across assets and timeframes; not % or σ, but visually consistent
---
### Optional
* Clamp: enable ±20 (or ±25) for strict bounded range (useful for public charts)
---
### Quick setups
**BTC Daily (“cycle view”)**
* Normalization: None
* Blend: 50%
* Smooth: 20
* Levels: ±12–15
**BTC 4H (“swing”)**
* Normalization: Z-score
* Window: 200
* Smooth: 16
* Levels: ±2.5σ to ±3σ
**Alts 1H (“volatile”)**
* Normalization: Robust Z (MAD)
* Window: 200
* Smooth: 12
* Levels: ±3.5 to ±4.5
**Mixed assets 15m (“compact panel”)**
* Normalization: Tanh squash
* Scale: 8
* Smooth: 10
* Levels: ±8–12
* Clamp: ±20
Time Range Marker By BCB ElevateThe Time Range Marker is a simple yet powerful visual tool for traders who want to focus on specific time intervals within the trading day. This indicator highlights a custom time range on your chart using a background color, helping you visually isolate key trading sessions or event windows such as:
Market open/close hours
News release periods
High-volatility trading zones
Personal strategy testing windows
⚙️ Key Features:
Customizable start and end time (hour & minute)
Works across all intraday timeframes
Adjustable highlight color to match your chart theme
Built using Pine Script v5 for speed and flexibility
🔧 Settings:
Start Hour / Minute – Set the beginning of the time range (in 24-hour format)
End Hour / Minute – Define when the range ends
Highlight Color – Choose the background color for better visibility
🕒 Timezone Note:
The indicator uses UTC time by default to ensure accuracy across markets. If your broker uses a different timezone (like EST, IST, etc.), the script can be adjusted to reflect your local market hours.
✅ How to Use the Time Range Marker Indicator
This indicator is used to visually highlight a specific time window each trading day, such as:
Market open or close sessions (e.g., NYSE, London, Tokyo)
High-impact news release periods
Custom time slots for strategy testing or scalping
🛠️ Installation Steps
Open TradingView and go to any chart.
Click on Pine Editor at the bottom of the screen.
Copy and paste the full Pine Script (shared above) into the editor.
Click the “Add to Chart” ▶️ button.
The indicator will appear on the chart with a highlighted background during the time range you set.
⚙️ How to Customize the Time Range
After adding the indicator:
Click the gear icon ⚙️ next to the indicator’s name on the chart.
Adjust the following settings:
Start Hour / Start Minute: The beginning of your time range (in 24-hour format).
End Hour / End Minute: When the highlight should stop.
Highlight Color: Pick a color and transparency for visual clarity.
Click OK to apply changes.
🕒 Timezone Consideration
By default, the indicator uses UTC (Coordinated Universal Time).
To match your broker’s timezone (e.g., EST, IST, etc.), you'll need to adjust the script by changing:
sessStart = timestamp("Etc/UTC", ...)
sessEnd = timestamp("Etc/UTC", ...)
to your correct timezone, like "Asia/Kolkata" for IST or "America/New_York" for EST.
Let me know your broker or local timezone, and I’ll update it for you.
📈 Tips for Traders
Combine this with volume, price action, or breakout indicators to focus your strategy on high-probability time windows.
Use multiple versions of this script if you want to highlight more than one time range in a day.
Time Appliconic Macro | ForTF5m (Fixed)The Time Appliconic Macro (TAMcr) is a custom-built trading indicator designed for the 5-minute time frame (TF5m), providing traders with clear Buy and Sell signals based on precise technical conditions and specific time windows.
Key Features:
Dynamic Moving Average (MA):
The indicator utilizes a Simple Moving Average (SMA) to identify price trends.
Adjustable length for user customization.
Custom STARC Bands:
Upper and lower bands are calculated using the SMA and the Average True Range (ATR).
Includes a user-defined multiplier to adjust the band width for flexibility across different market conditions.
RSI Integration:
Signals are filtered using the Relative Strength Index (RSI), ensuring they align with overbought/oversold conditions.
Time-Based Signal Filtering:
Signals are generated only during specific time windows, allowing traders to focus on high-activity periods or times of personal preference.
Supports multiple custom time ranges with automatic adjustments for UTC-4 or UTC-5 offsets.
Clear Signal Visualization:
Buy Signals: Triggered when the price is below the lower band, RSI indicates oversold conditions, and the time is within the defined range.
Sell Signals: Triggered when the price is above the upper band, RSI indicates overbought conditions, and the time is within the defined range.
Signals are marked directly on the chart for easy identification.
Customizability:
Adjustable parameters for the Moving Average length, ATR length, and ATR multiplier.
Time zone selection and defined trading windows provide a tailored experience for global users.
Who is this Indicator For?
This indicator is perfect for intraday traders who operate in the 5-minute time frame and value clear, filtered signals based on price action, volatility, and momentum indicators. The time window functionality is ideal for traders focusing on specific market sessions or personal schedules.
How to Use:
Adjust the MA and ATR parameters to match your trading style or market conditions.
Set the desired time zone and time ranges to align with your preferred trading hours.
Monitor the chart for Buy (green) and Sell (red) signals, and use them as a guide for entering or exiting trades.
Machine Learning: Gaussian Process Regression [LuxAlgo]We provide an implementation of the Gaussian Process Regression (GPR), a popular machine-learning method capable of estimating underlying trends in prices as well as forecasting them.
While this implementation is adapted to real-time usage, do remember that forecasting trends in the market is challenging, do not use this tool as a standalone for your trading decisions.
🔶 USAGE
The main goal of our implementation of GPR is to forecast trends. The method is applied to a subset of the most recent prices, with the Training Window determining the size of this subset.
Two user settings controlling the trend estimate are available, Smooth and Sigma . Smooth determines the smoothness of our estimate, with higher values returning smoother results suitable for longer-term trend estimates.
Sigma controls the amplitude of the forecast, with values closer to 0 returning results with a higher amplitude. Do note that due to the calculation of the method, lower values of sigma can return errors with higher values of the training window.
🔹 Updating Mechanisms
The script includes three methods to update a forecast. By default a forecast will not update for new bars (Lock Forecast).
The forecast can be re-estimated once the price reaches the end of the forecasting window when using the "Update Once Reached" method.
Finally "Continuously Update" will update the whole forecast on any new bar.
🔹 Estimating Trends
Gaussian Process Regression can be used to estimate past underlying local trends in the price, allowing for a noise-free interpretation of trends.
This can be useful for performing descriptive analysis, such as highlighting patterns more easily.
🔶 SETTINGS
Training Window: Number of most recent price observations used to fit the model
Forecasting Length: Forecasting horizon, determines how many bars in the future are forecasted.
Smooth: Controls the degree of smoothness of the model fit.
Sigma: Noise variance. Controls the amplitude of the forecast, lower values will make it more sensitive to outliers.
Update: Determines when the forecast is updated, by default the forecast is not updated for new bars.
theme_presetsStyle Made Easy with 175 Reversable light/dark themes
Built on to of my theme engine, so any tools built with one
will work with the other.
getTheme(_input)
Get a theme by name. (see lib for copy/paste list)
Parameters:
_input : string Name of Theme to use.
apathy()
Theme preset -> "Apathy"
Returns: Theme object
apprentice()
Theme preset -> "Apprentice"
Returns: Theme object
ashes()
Theme preset -> "Ashes"
Returns: Theme object
atelier_cave()
Theme preset -> "Atelier Cave"
Returns: Theme object
atelier_dune()
Theme preset -> "Atelier Dune"
Returns: Theme object
atelier_estuary()
Theme preset -> "Atelier Estuary"
Returns: Theme object
atelier_forest()
Theme preset -> "Atelier Forest"
Returns: Theme object
atelier_heath()
Theme preset -> "Atelier Heath"
Returns: Theme object
atelier_lakeside()
Theme preset -> "Atelier Lakeside"
Returns: Theme object
atelier_plateau()
Theme preset -> "Atelier Plateau"
Returns: Theme object
atelier_savanna()
Theme preset -> "Atelier Savanna"
Returns: Theme object
atelier_seaside()
Theme preset -> "Atelier Seaside"
Returns: Theme object
atelier_sulphurpool()
Theme preset -> "Atelier Sulphurpool"
Returns: Theme object
atlas()
Theme preset -> "Atlas"
Returns: Theme object
ayu()
Theme preset -> "Ayu"
Returns: Theme object
ayu_mirage()
Theme preset -> "Ayu Mirage"
Returns: Theme object
bespin()
Theme preset -> "Bespin"
Returns: Theme object
black_metal()
Theme preset -> "Black Metal"
Returns: Theme object
black_metal_bathory()
Theme preset -> "Black Metal (bathory)"
Returns: Theme object
black_metal_burzum()
Theme preset -> "Black Metal (burzum)"
Returns: Theme object
black_metal_funeral()
Theme preset -> "Black Metal (dark Funeral)"
Returns: Theme object
black_metal_gorgoroth()
Theme preset -> "Black Metal (gorgoroth)"
Returns: Theme object
black_metal_immortal()
Theme preset -> "Black Metal (immortal)"
Returns: Theme object
black_metal_khold()
Theme preset -> "Black Metal (khold)"
Returns: Theme object
black_metal_marduk()
Theme preset -> "Black Metal (marduk)"
Returns: Theme object
black_metal_mayhem()
Theme preset -> "Black Metal (mayhem)"
Returns: Theme object
black_metal_nile()
Theme preset -> "Black Metal (nile)"
Returns: Theme object
black_metal_venom()
Theme preset -> "Black Metal (venom)"
Returns: Theme object
blue_forest()
Theme preset -> "Blue Forest"
Returns: Theme object
blueish()
Theme preset -> "Blueish"
Returns: Theme object
brewer()
Theme preset -> "Brewer"
Returns: Theme object
bright()
Theme preset -> "Bright"
Returns: Theme object
brogrammer()
Theme preset -> "Brogrammer"
Returns: Theme object
brush_trees()
Theme preset -> "Brush Trees"
Returns: Theme object
catppuccin()
Theme preset -> "Catppuccin"
Returns: Theme object
chalk()
Theme preset -> "Chalk"
Returns: Theme object
circus()
Theme preset -> "Circus"
Returns: Theme object
classic()
Theme preset -> "Classic"
Returns: Theme object
clrs()
Theme preset -> "Colors"
Returns: Theme object
codeschool()
Theme preset -> "Codeschool"
Returns: Theme object
cupcake()
Theme preset -> "Cupcake"
Returns: Theme object
cupertino()
Theme preset -> "Cupertino"
Returns: Theme object
da_one_black()
Theme preset -> "Da One Black"
Returns: Theme object
da_one_gray()
Theme preset -> "Da One Gray"
Returns: Theme object
da_one_ocean()
Theme preset -> "Da One Ocean"
Returns: Theme object
da_one_paper()
Theme preset -> "Da One Paper"
Returns: Theme object
da_one_sea()
Theme preset -> "Da One Sea"
Returns: Theme object
da_one_white()
Theme preset -> "Da One White"
Returns: Theme object
danqing()
Theme preset -> "Danqing"
Returns: Theme object
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ConditionalAverages█ OVERVIEW
This library is a Pine Script™ programmer’s tool containing functions that average values selectively.
█ CONCEPTS
Averaging can be useful to smooth out unstable readings in the data set, provide a benchmark to see the underlying trend of the data, or to provide a general expectancy of values in establishing a central tendency. Conventional averaging techniques tend to apply indiscriminately to all values in a fixed window, but it can sometimes be useful to average values only when a specific condition is met. As conditional averaging works on specific elements of a dataset, it can help us derive more context-specific conclusions. This library offers a collection of averaging methods that not only accomplish these tasks, but also exploit the efficiencies of the Pine Script™ runtime by foregoing unnecessary and resource-intensive for loops.
█ NOTES
To Loop or Not to Loop
Though for and while loops are essential programming tools, they are often unnecessary in Pine Script™. This is because the Pine Script™ runtime already runs your scripts in a loop where it executes your code on each bar of the dataset. Pine Script™ programmers who understand how their code executes on charts can use this to their advantage by designing loop-less code that will run orders of magnitude faster than functionally identical code using loops. Most of this library's function illustrate how you can achieve loop-less code to process past values. See the User Manual page on loops for more information. If you are looking for ways to measure execution time for you scripts, have a look at our LibraryStopwatch library .
Our `avgForTimeWhen()` and `totalForTimeWhen()` are exceptions in the library, as they use a while structure. Only a few iterations of the loop are executed on each bar, however, as its only job is to remove the few elements in the array that are outside the moving window defined by a time boundary.
Cumulating and Summing Conditionally
The ta.cum() or math.sum() built-in functions can be used with ternaries that select only certain values. In our `avgWhen(src, cond)` function, for example, we use this technique to cumulate only the occurrences of `src` when `cond` is true:
float cumTotal = ta.cum(cond ? src : 0) We then use:
float cumCount = ta.cum(cond ? 1 : 0) to calculate the number of occurrences where `cond` is true, which corresponds to the quantity of values cumulated in `cumTotal`.
Building Custom Series With Arrays
The advent of arrays in Pine has enabled us to build our custom data series. Many of this library's functions use arrays for this purpose, saving newer values that come in when a condition is met, and discarding the older ones, implementing a queue .
`avgForTimeWhen()` and `totalForTimeWhen()`
These two functions warrant a few explanations. They operate on a number of values included in a moving window defined by a timeframe expressed in milliseconds. We use a 1D timeframe in our example code. The number of bars included in the moving window is unknown to the programmer, who only specifies the period of time defining the moving window. You can thus use `avgForTimeWhen()` to calculate a rolling moving average for the last 24 hours, for example, that will work whether the chart is using a 1min or 1H timeframe. A 24-hour moving window will typically contain many more values on a 1min chart that on a 1H chart, but their calculated average will be very close.
Problems will arise on non-24x7 markets when large time gaps occur between chart bars, as will be the case across holidays or trading sessions. For example, if you were using a 24H timeframe and there is a two-day gap between two bars, then no chart bars would fit in the moving window after the gap. The `minBars` parameter mitigates this by guaranteeing that a minimum number of bars are always included in the calculation, even if including those bars requires reaching outside the prescribed timeframe. We use a minimum value of 10 bars in the example code.
Using var in Constant Declarations
In the past, we have been using var when initializing so-called constants in our scripts, which as per the Style Guide 's recommendations, we identify using UPPER_SNAKE_CASE. It turns out that var variables incur slightly superior maintenance overhead in the Pine Script™ runtime, when compared to variables initialized on each bar. We thus no longer use var to declare our "int/float/bool" constants, but still use it when an initialization on each bar would require too much time, such as when initializing a string or with a heavy function call.
Look first. Then leap.
█ FUNCTIONS
avgWhen(src, cond)
Gathers values of the source when a condition is true and averages them over the total number of occurrences of the condition.
Parameters:
src : (series int/float) The source of the values to be averaged.
cond : (series bool) The condition determining when a value will be included in the set of values to be averaged.
Returns: (float) A cumulative average of values when a condition is met.
avgWhenLast(src, cond, cnt)
Gathers values of the source when a condition is true and averages them over a defined number of occurrences of the condition.
Parameters:
src : (series int/float) The source of the values to be averaged.
cond : (series bool) The condition determining when a value will be included in the set of values to be averaged.
cnt : (simple int) The quantity of last occurrences of the condition for which to average values.
Returns: (float) The average of `src` for the last `x` occurrences where `cond` is true.
avgWhenInLast(src, cond, cnt)
Gathers values of the source when a condition is true and averages them over the total number of occurrences during a defined number of bars back.
Parameters:
src : (series int/float) The source of the values to be averaged.
cond : (series bool) The condition determining when a value will be included in the set of values to be averaged.
cnt : (simple int) The quantity of bars back to evaluate.
Returns: (float) The average of `src` in last `cnt` bars, but only when `cond` is true.
avgSince(src, cond)
Averages values of the source since a condition was true.
Parameters:
src : (series int/float) The source of the values to be averaged.
cond : (series bool) The condition determining when the average is reset.
Returns: (float) The average of `src` since `cond` was true.
avgForTimeWhen(src, ms, cond, minBars)
Averages values of `src` when `cond` is true, over a moving window of length `ms` milliseconds.
Parameters:
src : (series int/float) The source of the values to be averaged.
ms : (simple int) The time duration in milliseconds defining the size of the moving window.
cond : (series bool) The condition determining which values are included. Optional.
minBars : (simple int) The minimum number of values to keep in the moving window. Optional.
Returns: (float) The average of `src` when `cond` is true in the moving window.
totalForTimeWhen(src, ms, cond, minBars)
Sums values of `src` when `cond` is true, over a moving window of length `ms` milliseconds.
Parameters:
src : (series int/float) The source of the values to be summed.
ms : (simple int) The time duration in milliseconds defining the size of the moving window.
cond : (series bool) The condition determining which values are included. Optional.
minBars : (simple int) The minimum number of values to keep in the moving window. Optional.
Returns: (float) The sum of `src` when `cond` is true in the moving window.
KST Strategy [Skyrexio]Overview
KST Strategy leverages Know Sure Thing (KST) indicator in conjunction with the Williams Alligator and Moving average to obtain the high probability setups. KST is used for for having the high probability to enter in the direction of a current trend when momentum is rising, Alligator is used as a short term trend filter, while Moving average approximates the long term trend and allows trades only in its direction. Also strategy has the additional optional filter on Choppiness Index which does not allow trades if market is choppy, above the user-specified threshold. Strategy has the user specified take profit and stop-loss numbers, but multiplied by Average True Range (ATR) value on the moment when trade is open. The strategy opens only long trades.
Unique Features
ATR based stop-loss and take profit. Instead of fixed take profit and stop-loss percentage strategy utilizes user chosen numbers multiplied by ATR for its calculation.
Configurable Trading Periods. Users can tailor the strategy to specific market windows, adapting to different market conditions.
Optional Choppiness Index filter. Strategy allows to choose if it will use the filter trades with Choppiness Index and set up its threshold.
Methodology
The strategy opens long trade when the following price met the conditions:
Close price is above the Alligator's jaw line
Close price is above the filtering Moving average
KST line of Know Sure Thing indicator shall cross over its signal line (details in justification of methodology)
If the Choppiness Index filter is enabled its value shall be less than user defined threshold
When the long trade is executed algorithm defines the stop-loss level as the low minus user defined number, multiplied by ATR at the trade open candle. Also it defines take profit with close price plus user defined number, multiplied by ATR at the trade open candle. While trade is in progress, if high price on any candle above the calculated take profit level or low price is below the calculated stop loss level, trade is closed.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.5, number of ATRs to calculate stop-loss level)
ATR Take Profit (by default = 3.5, number of ATRs to calculate take profit level)
Filter MA Type (by default = Least Squares MA, type of moving average which is used for filter MA)
Filter MA Length (by default = 200, length for filter MA calculation)
Enable Choppiness Index Filter (by default = true, setting to choose the optional filtering using Choppiness index)
Choppiness Index Threshold (by default = 50, Choppiness Index threshold, its value shall be below it to allow trades execution)
Choppiness Index Length (by default = 14, length used in Choppiness index calculation)
KST ROC Length #1 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST ROC Length #2 (by default = 15, value used in KST indicator calculation, more information in Justification of Methodology)
KST ROC Length #3 (by default = 20, value used in KST indicator calculation, more information in Justification of Methodology)
KST ROC Length #4 (by default = 30, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #1 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #2 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #3 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #4 (by default = 15, value used in KST indicator calculation, more information in Justification of Methodology)
KST Signal Line Length (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is KST, Williams Alligator, Moving Average, ATR and Choppiness Index.
The KST (Know Sure Thing) is a momentum oscillator developed by Martin Pring. It combines multiple Rate of Change (ROC) values, smoothed over different timeframes, to identify trend direction and momentum strength. First of all, what is ROC? ROC (Rate of Change) is a momentum indicator that measures the percentage change in price between the current price and the price a set number of periods ago.
ROC = 100 * (Current Price - Price N Periods Ago) / Price N Periods Ago
In our case N is the KST ROC Length inputs from settings, here we will calculate 4 different ROCs to obtain KST value:
KST = ROC1_smooth × 1 + ROC2_smooth × 2 + ROC3_smooth × 3 + ROC4_smooth × 4
ROC1 = ROC(close, KST ROC Length #1), smoothed by KST SMA Length #1,
ROC2 = ROC(close, KST ROC Length #2), smoothed by KST SMA Length #2,
ROC3 = ROC(close, KST ROC Length #3), smoothed by KST SMA Length #3,
ROC4 = ROC(close, KST ROC Length #4), smoothed by KST SMA Length #4
Also for this indicator the signal line is calculated:
Signal = SMA(KST, KST Signal Line Length)
When the KST line rises, it indicates increasing momentum and suggests that an upward trend may be developing. Conversely, when the KST line declines, it reflects weakening momentum and a potential downward trend. A crossover of the KST line above its signal line is considered a buy signal, while a crossover below the signal line is viewed as a sell signal. If the KST stays above zero, it indicates overall bullish momentum; if it remains below zero, it points to bearish momentum. The KST indicator smooths momentum across multiple timeframes, helping to reduce noise and provide clearer signals for medium- to long-term trends.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
The next indicator is Moving Average. It has a lot of different types which can be chosen to filter trades and the Least Squares MA is used by default settings. Let's briefly explain what is it.
The Least Squares Moving Average (LSMA) — also known as Linear Regression Moving Average — is a trend-following indicator that uses the least squares method to fit a straight line to the price data over a given period, then plots the value of that line at the most recent point. It draws the best-fitting straight line through the past N prices (using linear regression), and then takes the endpoint of that line as the value of the moving average for that bar. The LSMA aims to reduce lag and highlight the current trend more accurately than traditional moving averages like SMA or EMA.
Key Features:
It reacts faster to price changes than most moving averages.
It is smoother and less noisy than short-term EMAs.
It can be used to identify trend direction, momentum, and potential reversal points.
ATR (Average True Range) is a volatility indicator that measures how much an asset typically moves during a given period. It was introduced by J. Welles Wilder and is widely used to assess market volatility, not direction.
To calculate it first of all we need to get True Range (TR), this is the greatest value among:
High - Low
abs(High - Previous Close)
abs(Low - Previous Close)
ATR = MA(TR, n) , where n is number of periods for moving average, in our case equals 14.
ATR shows how much an asset moves on average per candle/bar. A higher ATR means more volatility; a lower ATR means a calmer market.
The Choppiness Index is a technical indicator that quantifies whether the market is trending or choppy (sideways). It doesn't indicate trend direction — only the strength or weakness of a trend. Higher Choppiness Index usually approximates the sideways market, while its low value tells us that there is a high probability of a trend.
Choppiness Index = 100 × log10(ΣATR(n) / (MaxHigh(n) - MinLow(n))) / log10(n)
where:
ΣATR(n) = sum of the Average True Range over n periods
MaxHigh(n) = highest high over n periods
MinLow(n) = lowest low over n periods
log10 = base-10 logarithm
Now let's understand how these indicators work in conjunction and why they were chosen for this strategy. KST indicator approximates current momentum, when it is rising and KST line crosses over the signal line there is high probability that short term trend is reversing to the upside and strategy allows to take part in this potential move. Alligator's jaw (blue) line is used as an approximation of a short term trend, taking trades only above it we want to avoid trading against trend to increase probability that long trade is going to be winning.
Almost the same for Moving Average, but it approximates the long term trend, this is just the additional filter. If we trade in the direction of the long term trend we increase probability that higher risk to reward trade will hit the take profit. Choppiness index is the optional filter, but if it turned on it is used for approximating if now market is in sideways or in trend. On the range bounded market the potential moves are restricted. We want to decrease probability opening trades in such condition avoiding trades if this index is above threshold value.
When trade is open script sets the stop loss and take profit targets. ATR approximates the current volatility, so we can make a decision when to exit a trade based on current market condition, it can increase the probability that strategy will avoid the excessive stop loss hits, but anyway user can setup how many ATRs to use as a stop loss and take profit target. As was said in the Methodology stop loss level is obtained by subtracting number of ATRs from trade opening candle low, while take profit by adding to this candle's close.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2025.05.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 60%
Maximum Single Position Loss: -5.53%
Maximum Single Profit: +8.35%
Net Profit: +5175.20 USDT (+51.75%)
Total Trades: 120 (56.67% win rate)
Profit Factor: 1.747
Maximum Accumulated Loss: 1039.89 USDT (-9.1%)
Average Profit per Trade: 43.13 USDT (+0.6%)
Average Trade Duration: 27 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 1h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrexio commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation.
Euclidean Range [InvestorUnknown]The Euclidean Range indicator visualizes price deviation from a moving average using a geometric concept Euclidean distance. It helps traders identify trend strength, volatility shifts, and potential overextensions in price behavior.
Euclidean Distance
Euclidean distance is a fundamental concept in geometry and machine learning. It measures the "straight-line distance" between two points in space. In time series analysis, it can be used to measure how far one sequence deviates from another over a fixed window.
euclidean_distance(src, ref, len) =>
var float sum_sq_diff = na
sum_sq_diff := 0.0
for i = 0 to len - 1
diff = src - ref
sum_sq_diff += diff * diff
math.sqrt(sum_sq_diff)
In this script, we calculate the Euclidean distance between the price (source) and a smoothed average (reference) over a user-defined window. This gives us a single scalar that reflects the overall divergence between price and trend.
How It Works
Moving Average Calculation: You can choose between SMA, EMA, or HMA as your reference line. This becomes the "baseline" against which the actual price is compared.
Distance Band Construction: The Euclidean distance between the price and the reference is calculated over the Window Length. This value is then added to and subtracted from the average to form dynamic upper and lower bands, visually framing the range of deviation.
Distance Ratios and Z-Scores: Two distance ratios are computed: dist_r = distance / price (sensitivity to volatility); dist_v = price / distance (sensitivity to compression or low-volatility states)
Both ratios are normalized using a Z-score to standardize their behavior and allow for easier interpretation across different assets and timeframes.
Z-Score Plots: Z_r (white line) highlights instances of high volatility or strong price deviation; Z_v (red line) highlights low volatility or compressed price ranges.
Background Highlighting (Optional): When Z_v is dominant and increasing, the background is colored using a gradient. This signals a possible build-up in low volatility, which may precede a breakout.
Use Cases
Detect volatile expansions and calm compression zones.
Identify mean reversion setups when price returns to the average.
Anticipate breakout conditions by observing rising Z_v values.
Use dynamic distance bands as adaptive support/resistance zones.
Notes
The indicator is best used with liquid assets and medium-to-long windows.
Background coloring helps visually filter for squeeze setups.
Disclaimer
This indicator is provided for speculative analysis and educational purposes only. It is not financial advice. Always backtest and evaluate in a simulated environment before live trading.
AO/AC Trading Zones Strategy [Skyrexio] Overview
AO/AC Trading Zones Strategy leverages the combination of Awesome Oscillator (AO), Acceleration/Deceleration Indicator (AC), Williams Fractals, Williams Alligator and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Combination of AO and AC is used for creating so-called trading zones to create the signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over. In some special cases strategy uses AO and AC combination to trail profit (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Both AC and AO shall print two consecutive increasing values. At the price candle close which corresponds to this condition algorithm opens the first long trade with 10% of capital.
4. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
5. If AO and AC both continue printing the rising values strategy opens the long trade on each candle close with 10% of capital while number of opened trades reaches 5.
6. If AO and AC both has printed 5 rising values in a row algorithm close all trades if candle's low below the low of the 5-th candle with rising AO and AC values in a row.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting:
EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation).
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's explore the key concepts of this strategy and understand how they work together. We'll begin with the simplest: the EMA.
The Exponential Moving Average (EMA) is a type of moving average that assigns greater weight to recent price data, making it more responsive to current market changes compared to the Simple Moving Average (SMA). This tool is widely used in technical analysis to identify trends and generate buy or sell signals. The EMA is calculated as follows:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy, the EMA acts as a long-term trend filter. For instance, long trades are considered only when the price closes above the EMA (default: 100-period). This increases the likelihood of entering trades aligned with the prevailing trend.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
Fractals, another tool by Bill Williams, help identify potential reversal points on a price chart. A fractal forms over at least five consecutive bars, with the middle bar showing either:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often use fractals alongside other indicators to confirm trends or reversals, enhancing decision-making accuracy.
How do these tools work together in this strategy? Let’s consider an example of an uptrend.
When the price breaks above an up fractal, it signals a potential bullish trend. This occurs because the up fractal represents a shift in market behavior, where a temporary high was formed due to selling pressure. If the price revisits this level and breaks through, it suggests the market sentiment has turned bullish.
The breakout must occur above the Alligator’s teeth line to confirm the trend. A breakout below the teeth is considered invalid, and the downtrend might still persist. Conversely, in a downtrend, the same logic applies with down fractals.
In this strategy if the most recent up fractal breakout occurs above the Alligator's teeth and follows the last down fractal breakout below the teeth, the algorithm identifies an uptrend. Long trades can be opened during this phase if a signal aligns. If the price breaks a down fractal below the teeth line during an uptrend, the strategy assumes the uptrend has ended and closes all open long trades.
By combining the EMA as a long-term trend filter with the Alligator and fractals as short-term filters, this approach increases the likelihood of opening profitable trades while staying aligned with market dynamics.
Now let's talk about the trading zones concept and its signals. To understand this we need to briefly introduce what is AO and AC. The Awesome Oscillator (AO), developed by Bill Williams, is a momentum indicator designed to measure market momentum by contrasting recent price movements with a longer-term historical perspective. It helps traders detect potential trend reversals and assess the strength of ongoing trends.
The formula for AO is as follows:
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
The Acceleration/Deceleration (AC) Indicator, introduced by Bill Williams, measures the rate of change in market momentum. It highlights shifts in the driving force of price movements and helps traders spot early signs of trend changes. The AC Indicator is particularly useful for identifying whether the current momentum is accelerating or decelerating, which can indicate potential reversals or continuations. For AC calculation we shall use the AO calculated above is the following formula:
AC = AO − SMA5(AO) , where SMA5(AO)is the 5-period Simple Moving Average of the Awesome Oscillator
When the AC is above the zero line and rising, it suggests accelerating upward momentum.
When the AC is below the zero line and falling, it indicates accelerating downward momentum.
When the AC is below zero line and rising it suggests the decelerating the downtrend momentum. When AC is above the zero line and falling, it suggests the decelerating the uptrend momentum.
Now let's discuss the trading zones concept and how it can create the signal. Zones are created by the combination of AO and AC. We can divide three zone types:
Greed zone: when the AO and AC both are rising
Red zone: when the AO and AC both are decreasing
Gray zone: when one of AO or AC is rising, the other is falling
Gray zone is considered as uncertainty. AC and AO are moving in the opposite direction. Strategy skip such price action to decrease the chance to stuck in the losing trade during potential sideways. Red zone is also not interesting for the algorithm because both indicators consider the trend as bearish, but strategy opens only long trades. It is waiting for the green zone to increase the chance to open trade in the direction of the potential uptrend. When we have 2 candles in a row in the green zone script executes a long trade with 10% of capital.
Two green zone candles in a row is considered by algorithm as a bullish trend, but now so strong, that's the reason why trade is going to be closed when the combination of Alligator and Fractals will consider the the trend change from bullish to bearish. If id did not happens, algorithm starts to count the green zone candles in a row. When we have 5 in a row script change the trade closing condition. Such situation is considered is a high probability strong bull market and all trades will be closed if candle's low will be lower than fifth green zone candle's low. This is used to increase probability to secure the profit. If long trades are initiated, the strategy continues utilizing subsequent signals until the total number of trades reaches a maximum of 5. Each trade uses 10% of capital.
Why we use trading zones signals? If currently strategy algorithm considers the high probability of the short-term uptrend with the Alligator and Fractals combination pointed out above and the long-term trend is also suggested by the EMA filter as bullish. Rising AC and AO values in the direction of the most likely main trend signaling that we have the high probability of the fastest bullish phase on the market. The main idea is to take part in such rapid moves and add trades if this move continues its acceleration according to indicators.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.12.31. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -9.49%
Maximum Single Profit: +24.33%
Net Profit: +4374.70 USDT (+43.75%)
Total Trades: 278 (39.57% win rate)
Profit Factor: 2.203
Maximum Accumulated Loss: 668.16 USDT (-5.43%)
Average Profit per Trade: 15.74 USDT (+1.37%)
Average Trade Duration: 60 hours
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
MultiLayer Acceleration/Deceleration Strategy [Skyrexio]Overview
MultiLayer Acceleration/Deceleration Strategy leverages the combination of Acceleration/Deceleration Indicator(AC), Williams Alligator, Williams Fractals and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Acceleration/Deceleration Indicator is used for creating signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Acceleration/Deceleration shall create one of two types of long signals (all details in "Justification of Methodology" paragraph). Buy stop order is placed one tick above the candle's high of last created long signal.
4. If price reaches the order price, long position is opened with 10% of capital.
5. If currently we have opened position and price creates and hit the order price of another one long signal, another one long position will be added to the previous with another one 10% of capital. Strategy allows to open up to 5 long trades simultaneously.
6. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting: EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation). User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's explore the key concepts of this strategy and understand how they work together. We'll begin with the simplest: the EMA.
The Exponential Moving Average (EMA) is a type of moving average that assigns greater weight to recent price data, making it more responsive to current market changes compared to the Simple Moving Average (SMA). This tool is widely used in technical analysis to identify trends and generate buy or sell signals. The EMA is calculated as follows:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy, the EMA acts as a long-term trend filter. For instance, long trades are considered only when the price closes above the EMA (default: 100-period). This increases the likelihood of entering trades aligned with the prevailing trend.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
Fractals, another tool by Bill Williams, help identify potential reversal points on a price chart. A fractal forms over at least five consecutive bars, with the middle bar showing either:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often use fractals alongside other indicators to confirm trends or reversals, enhancing decision-making accuracy.
How do these tools work together in this strategy? Let’s consider an example of an uptrend.
When the price breaks above an up fractal, it signals a potential bullish trend. This occurs because the up fractal represents a shift in market behavior, where a temporary high was formed due to selling pressure. If the price revisits this level and breaks through, it suggests the market sentiment has turned bullish.
The breakout must occur above the Alligator’s teeth line to confirm the trend. A breakout below the teeth is considered invalid, and the downtrend might still persist. Conversely, in a downtrend, the same logic applies with down fractals.
In this strategy if the most recent up fractal breakout occurs above the Alligator's teeth and follows the last down fractal breakout below the teeth, the algorithm identifies an uptrend. Long trades can be opened during this phase if a signal aligns. If the price breaks a down fractal below the teeth line during an uptrend, the strategy assumes the uptrend has ended and closes all open long trades.
By combining the EMA as a long-term trend filter with the Alligator and fractals as short-term filters, this approach increases the likelihood of opening profitable trades while staying aligned with market dynamics.
Now let's talk about Acceleration/Deceleration signals. AC indicator is calculated using the Awesome Oscillator, so let's first of all briefly explain what is Awesome Oscillator and how it can be calculated. The Awesome Oscillator (AO), developed by Bill Williams, is a momentum indicator designed to measure market momentum by contrasting recent price movements with a longer-term historical perspective. It helps traders detect potential trend reversals and assess the strength of ongoing trends.
The formula for AO is as follows:
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
The Acceleration/Deceleration (AC) Indicator, introduced by Bill Williams, measures the rate of change in market momentum. It highlights shifts in the driving force of price movements and helps traders spot early signs of trend changes. The AC Indicator is particularly useful for identifying whether the current momentum is accelerating or decelerating, which can indicate potential reversals or continuations. For AC calculation we shall use the AO calculated above is the following formula:
AC = AO − SMA5(AO), where SMA5(AO)is the 5-period Simple Moving Average of the Awesome Oscillator
When the AC is above the zero line and rising, it suggests accelerating upward momentum.
When the AC is below the zero line and falling, it indicates accelerating downward momentum.
When the AC is below zero line and rising it suggests the decelerating the downtrend momentum. When AC is above the zero line and falling, it suggests the decelerating the uptrend momentum.
Now we can explain which AC signal types are used in this strategy. The first type of long signal is when AC value is below zero line. In this cases we need to see three rising bars on the histogram in a row after the falling one. The second type of signals occurs above the zero line. There we need only two rising AC bars in a row after the falling one to create the signal. The signal bar is the last green bar in this sequence. The strategy places the buy stop order one tick above the candle's high, which corresponds to the signal bar on AC indicator.
After that we can have the following scenarios:
Price hit the order on the next candle in this case strategy opened long with this price.
Price doesn't hit the order price, the next candle set lower high. If current AC bar is increasing buy stop order changes by the script to the high of this new bar plus one tick. This procedure repeats until price finally hit buy order or current AC bar become decreasing. In the second case buy order cancelled and strategy wait for the next AC signal.
If long trades are initiated, the strategy continues utilizing subsequent signals until the total number of trades reaches a maximum of 5. All open trades are closed when the trend shifts to a downtrend, as determined by the combination of the Alligator and Fractals described earlier.
Why we use AC signals? If currently strategy algorithm considers the high probability of the short-term uptrend with the Alligator and Fractals combination pointed out above and the long-term trend is also suggested by the EMA filter as bullish. Rising AC bars after period of falling AC bars indicates the high probability of local pull back end and there is a high chance to open long trade in the direction of the most likely main uptrend. The numbers of rising bars are different for the different AC values (below or above zero line). This is needed because if AC below zero line the local downtrend is likely to be stronger and needs more rising bars to confirm that it has been changed than if AC is above zero.
Why strategy use only 10% per signal? Sometimes we can see the false signals which appears on sideways. Not risking that much script use only 10% per signal. If the first long trade has been open and price continue going up and our trend approximation by Alligator and Fractals is uptrend, strategy add another one 10% of capital to every next AC signal while number of active trades no more than 5. This capital allocation allows to take part in long trades when current uptrend is likely to be strong and use only 10% of capital when there is a high probability of sideways.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.11.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -5.15%
Maximum Single Profit: +24.57%
Net Profit: +2108.85 USDT (+21.09%)
Total Trades: 111 (36.94% win rate)
Profit Factor: 2.391
Maximum Accumulated Loss: 367.61 USDT (-2.97%)
Average Profit per Trade: 19.00 USDT (+1.78%)
Average Trade Duration: 75 hours
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 3h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
MultiLayer Awesome Oscillator Saucer Strategy [Skyrexio]Overview
MultiLayer Awesome Oscillator Saucer Strategy leverages the combination of Awesome Oscillator (AO), Williams Alligator, Williams Fractals and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Awesome Oscillator is used for creating signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Awesome Oscillator shall create the "Saucer" long signal (all details in "Justification of Methodology" paragraph). Buy stop order is placed one tick above the candle's high of last created "Saucer signal".
4. If price reaches the order price, long position is opened with 10% of capital.
5. If currently we have opened position and price creates and hit the order price of another one "Saucer" signal another one long position will be added to the previous with another one 10% of capital. Strategy allows to open up to 5 long trades simultaneously.
6. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting: EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation). User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's go through all concepts used in this strategy to understand how they works together. Let's start from the easies one, the EMA. Let's briefly explain what is EMA. The Exponential Moving Average (EMA) is a type of moving average that gives more weight to recent prices, making it more responsive to current price changes compared to the Simple Moving Average (SMA). It is commonly used in technical analysis to identify trends and generate buy or sell signals. It can be calculated with the following steps:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy uses EMA an initial long term trend filter. It allows to open long trades only if price close above EMA (by default 50 period). It increases the probability of taking long trades only in the direction of the trend.
Let's go to the next, short-term trend filter which consists of Alligator and Fractals. Let's briefly explain what do these indicators means. The Williams Alligator, developed by Bill Williams, is a technical indicator designed to spot trends and potential market reversals. It uses three smoothed moving averages, referred to as the jaw, teeth, and lips:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When these lines diverge and are properly aligned, the "alligator" is considered "awake," signaling a strong trend. Conversely, when the lines overlap or intertwine, the "alligator" is "asleep," indicating a range-bound or sideways market. This indicator assists traders in identifying when to act on or avoid trades.
The Williams Fractals, another tool introduced by Bill Williams, are used to pinpoint potential reversal points on a price chart. A fractal forms when there are at least five consecutive bars, with the middle bar displaying the highest high (for an up fractal) or the lowest low (for a down fractal), relative to the two bars on either side.
Key Points:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often combine fractals with other indicators to confirm trends or reversals, improving the accuracy of trading decisions.
How we use their combination in this strategy? Let’s consider an uptrend example. A breakout above an up fractal can be interpreted as a bullish signal, indicating a high likelihood that an uptrend is beginning. Here's the reasoning: an up fractal represents a potential shift in market behavior. When the fractal forms, it reflects a pullback caused by traders selling, creating a temporary high. However, if the price manages to return to that fractal’s high and break through it, it suggests the market has "changed its mind" and a bullish trend is likely emerging.
The moment of the breakout marks the potential transition to an uptrend. It’s crucial to note that this breakout must occur above the Alligator's teeth line. If it happens below, the breakout isn’t valid, and the downtrend may still persist. The same logic applies inversely for down fractals in a downtrend scenario.
So, if last up fractal breakout was higher, than Alligator's teeth and it happened after last down fractal breakdown below teeth, algorithm considered current trend as an uptrend. During this uptrend long trades can be opened if signal was flashed. If during the uptrend price breaks down the down fractal below teeth line, strategy considered that uptrend is finished with the high probability and strategy closes all current long trades. This combination is used as a short term trend filter increasing the probability of opening profitable long trades in addition to EMA filter, described above.
Now let's talk about Awesome Oscillator's "Sauser" signals. Briefly explain what is the Awesome Oscillator. The Awesome Oscillator (AO), created by Bill Williams, is a momentum-based indicator that evaluates market momentum by comparing recent price activity to a broader historical context. It assists traders in identifying potential trend reversals and gauging trend strength.
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
Now we know what is AO, but what is the "Saucer" signal? This concept was introduced by Bill Williams, let's briefly explain it and how it's used by this strategy. Initially, this type of signal is a combination of the following AO bars: we need 3 bars in a row, the first one shall be higher than the second, the third bar also shall be higher, than second. All three bars shall be above the zero line of AO. The price bar, which corresponds to third "saucer's" bar is our signal bar. Strategy places buy stop order one tick above the price bar which corresponds to signal bar.
After that we can have the following scenarios.
Price hit the order on the next candle in this case strategy opened long with this price.
Price doesn't hit the order price, the next candle set lower low. If current AO bar is increasing buy stop order changes by the script to the high of this new bar plus one tick. This procedure repeats until price finally hit buy order or current AO bar become decreasing. In the second case buy order cancelled and strategy wait for the next "Saucer" signal.
If long trades has been opened strategy use all the next signals until number of trades doesn't exceed 5. All trades are closed when the trend changes to downtrend according to combination of Alligator and Fractals described above.
Why we use "Saucer" signals? If AO above the zero line there is a high probability that price now is in uptrend if we take into account our two trend filters. When we see the decreasing bars on AO and it's above zero it's likely can be considered as a pullback on the uptrend. When we see the stop of AO decreasing and the first increasing bar has been printed there is a high probability that this local pull back is finished and strategy open long trade in the likely direction of a main trend.
Why strategy use only 10% per signal? Sometimes we can see the false signals which appears on sideways. Not risking that much script use only 10% per signal. If the first long trade has been open and price continue going up and our trend approximation by Alligator and Fractals is uptrend, strategy add another one 10% of capital to every next saucer signal while number of active trades no more than 5. This capital allocation allows to take part in long trades when current uptrend is likely to be strong and use only 10% of capital when there is a high probability of sideways.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.11.25. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -5.10%
Maximum Single Profit: +22.80%
Net Profit: +2838.58 USDT (+28.39%)
Total Trades: 107 (42.99% win rate)
Profit Factor: 3.364
Maximum Accumulated Loss: 373.43 USDT (-2.98%)
Average Profit per Trade: 26.53 USDT (+2.40%)
Average Trade Duration: 78 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 3h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Triple CCI Strategy MFI Confirmed [Skyrexio]Overview
Triple CCI Strategy MFI Confirmed leverages 3 different periods Commodity Channel Index (CCI) indicator in conjunction Money Flow Index (MFI) and Exponential Moving Average (EMA) to obtain the high probability setups. Fast period CCI is used for having the high probability to enter in the direction of short term trend, middle and slow period CCI are used for confirmation, if market now likely in the mid and long-term uptrend. MFI is used to confirm trade with the money inflow/outflow with the high probability. EMA is used as an additional trend filter. Moreover, strategy uses exponential moving average (EMA) to trail the price when it reaches the specific level. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Four layers trade filtering system: Strategy utilizes two different period CCI indicators, MFI and EMA indicators to confirm the signals produced by fast period CCI.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
Fast period CCI shall crossover the zero-line.
Slow and Middle period CCI shall be above zero-lines.
Price shall close above the EMA. Crossover is not obligatory
MFI shall be above 50
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
CCI Fast Length (by default = 14, used for calculation short term period CCI)
CCI Middle Length (by default = 25, used for calculation short term period CCI)
CCI Slow Length (by default = 50, used for calculation long term period CCI)
MFI Length (by default = 14, used for calculation MFI
EMA Length (by default = 50, period of EMA, used for trend filtering EMA calculation)
Trailing EMA Length (by default = 20)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is CCI, MFI and EMA.
The Commodity Channel Index (CCI) is a momentum-based technical indicator that measures the deviation of a security's price from its average price over a specific period. It helps traders identify overbought or oversold conditions and potential trend reversals.
The CCI formula is:
CCI = (Typical Price − SMA) / (0.015 × Mean Deviation)
Typical Price (TP): This is calculated as the average of the high, low, and closing prices for the period.
Simple Moving Average (SMA): This is the average of the Typical Prices over a specific number of periods.
Mean Deviation: This is the average of the absolute differences between the Typical Price and the SMA.
The result is a value that typically fluctuates between +100 and -100, though it is not bounded and can go higher or lower depending on the price movement.
The Money Flow Index (MFI) is a technical indicator that measures the strength of money flowing into and out of a security. It combines price and volume data to assess buying and selling pressure and is often used to identify overbought or oversold conditions. The formula for MFI involves several steps:
1. Calculate the Typical Price (TP):
TP = (high + low + close) / 3
2. Calculate the Raw Money Flow (RMF):
Raw Money Flow = TP × Volume
3. Determine Positive and Negative Money Flow:
If the current TP is greater than the previous TP, it's Positive Money Flow.
If the current TP is less than the previous TP, it's Negative Money Flow.
4. Calculate the Money Flow Ratio (MFR):
Money Flow Ratio = Sum of Positive Money Flow (over n periods) / Sum of Negative Money Flow (over n periods)
5. Calculate the Money Flow Index (MFI):
MFI = 100 − (100 / (1 + Money Flow Ratio))
MFI above 80 can be considered as overbought, below 20 - oversold.
The Exponential Moving Average (EMA) is a type of moving average that places greater weight and significance on the most recent data points. It is widely used in technical analysis to smooth price data and identify trends more quickly than the Simple Moving Average (SMA).
Formula:
1. Calculate the multiplier
Multiplier = 2 / (n + 1) , Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
This strategy leverages Fast period CCI, which shall break the zero line to the upside to say that probability of short term trend change to the upside increased. This zero line crossover shall be confirmed by the Middle and Slow periods CCI Indicators. At the moment of breakout these two CCIs shall be above 0, indicating that there is a high probability that price is in middle and long term uptrend. This approach increases chances to have a long trade setup in the direction of mid-term and long-term trends when the short-term trend starts to reverse to the upside.
Additionally strategy uses MFI to have a greater probability that fast CCI breakout is confirmed by this indicator. We consider the values of MFI above 50 as a higher probability that trend change from downtrend to the uptrend is real. Script opens long trades only if MFI is above 50. As you already know from the MFI description, it incorporates volume in its calculation, therefore we have another one confirmation factor.
Finally, strategy uses EMA an additional trend filter. It allows to open long trades only if price close above EMA (by default 50 period). It increases the probability of taking long trades only in the direction of the trend.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements. It’s also important to make a note, that script uses another one EMA (by default = 20 period) as a trailing profit level.
Backtest Results
Operating window: Date range of backtests is 2022.04.01 - 2024.11.25. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 50%
Maximum Single Position Loss: -4.13%
Maximum Single Profit: +19.66%
Net Profit: +5421.21 USDT (+54.21%)
Total Trades: 108 (44.44% win rate)
Profit Factor: 2.006
Maximum Accumulated Loss: 777.40 USDT (-7.77%)
Average Profit per Trade: 50.20 USDT (+0.85%)
Average Trade Duration: 44 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
RSI Trend Following StrategyOverview
The RSI Trend Following Strategy utilizes Relative Strength Index (RSI) to enter the trade for the potential trend continuation. It uses Stochastic indicator to check is the price is not in overbought territory and the MACD to measure the current price momentum. Moreover, it uses the 200-period EMA to filter the counter trend trades with the higher probability. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Two layers trade filtering system: Strategy utilizes MACD and Stochastic indicators measure the current momentum and overbought condition and use 200-period EMA to filter trades against major trend.
Trailing take profit level: After reaching the trailing profit activation level script activates the trailing of long trade using EMA. More information in methodology.
Wide opportunities for strategy optimization: Flexible strategy settings allows users to optimize the strategy entries and exits for chosen trading pair and time frame.
Methodology
The strategy opens long trade when the following price met the conditions:
RSI is above 50 level.
MACD line shall be above the signal line
Both lines of Stochastic shall be not higher than 80 (overbought territory)
Candle’s low shall be above the 200 period EMA
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with trailing EMA(by default = 20 period). If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
MACD Fast Length (by default = 12, period of averaging fast MACD line)
MACD Fast Length (by default = 26, period of averaging slow MACD line)
MACD Signal Smoothing (by default = 9, period of smoothing MACD signal line)
Oscillator MA Type (by default = EMA, available options: SMA, EMA)
Signal Line MA Type (by default = EMA, available options: SMA, EMA)
RSI Length (by default = 14, period for RSI calculation)
Trailing EMA Length (by default = 20, period for EMA, which shall be broken close the trade after trailing profit activation)
Justification of Methodology
This trading strategy is designed to leverage a combination of technical indicators—Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Stochastic Oscillator, and the 200-period Exponential Moving Average (EMA)—to determine optimal entry points for long trades. Additionally, the strategy uses the Average True Range (ATR) for dynamic risk management to adapt to varying market conditions. Let's look in details for which purpose each indicator is used for and why it is used in this combination.
Relative Strength Index (RSI) is a momentum indicator used in technical analysis to measure the speed and change of price movements in a financial market. It helps traders identify whether an asset is potentially overbought (overvalued) or oversold (undervalued), which can indicate a potential reversal or continuation of the current trend.
How RSI Works? RSI tracks the strength of recent price changes. It compares the average gains and losses over a specific period (usually 14 periods) to assess the momentum of an asset. Average gain is the average of all positive price changes over the chosen period. It reflects how much the price has typically increased during upward movements. Average loss is the average of all negative price changes over the same period. It reflects how much the price has typically decreased during downward movements.
RSI calculates these average gains and losses and compares them to create a value between 0 and 100. If the RSI value is above 70, the asset is generally considered overbought, meaning it might be due for a price correction or reversal downward. Conversely, if the RSI value is below 30, the asset is considered oversold, suggesting it could be poised for an upward reversal or recovery. RSI is a useful tool for traders to determine market conditions and make informed decisions about entering or exiting trades based on the perceived strength or weakness of an asset's price movements.
This strategy uses RSI as a short-term trend approximation. If RSI crosses over 50 it means that there is a high probability of short-term trend change from downtrend to uptrend. Therefore RSI above 50 is our first trend filter to look for a long position.
The MACD (Moving Average Convergence Divergence) is a popular momentum and trend-following indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in an asset's price.
The MACD consists of three components:
MACD Line: This is the difference between a short-term Exponential Moving Average (EMA) and a long-term EMA, typically calculated as: MACD Line = 12 period EMA − 26 period EMA
Signal Line: This is a 9-period EMA of the MACD Line, which helps to identify buy or sell signals. When the MACD Line crosses above the Signal Line, it can be a bullish signal (suggesting a buy); when it crosses below, it can be a bearish signal (suggesting a sell).
Histogram: The histogram shows the difference between the MACD Line and the Signal Line, visually representing the momentum of the trend. Positive histogram values indicate increasing bullish momentum, while negative values indicate increasing bearish momentum.
This strategy uses MACD as a second short-term trend filter. When MACD line crossed over the signal line there is a high probability that uptrend has been started. Therefore MACD line above signal line is our additional short-term trend filter. In conjunction with RSI it decreases probability of following false trend change signals.
The Stochastic Indicator is a momentum oscillator that compares a security's closing price to its price range over a specific period. It's used to identify overbought and oversold conditions. The indicator ranges from 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions.
It consists of two lines:
%K: The main line, calculated using the formula (CurrentClose−LowestLow)/(HighestHigh−LowestLow)×100 . Highest and lowest price taken for 14 periods.
%D: A smoothed moving average of %K, often used as a signal line.
This strategy uses stochastic to define the overbought conditions. The logic here is the following: we want to avoid long trades in the overbought territory, because when indicator reaches it there is a high probability that the potential move is gonna be restricted.
The 200-period EMA is a widely recognized indicator for identifying the long-term trend direction. The strategy only trades in the direction of this primary trend to increase the probability of successful trades. For instance, when the price is above the 200 EMA, only long trades are considered, aligning with the overarching trend direction.
Therefore, strategy uses combination of RSI and MACD to increase the probability that price now is in short-term uptrend, Stochastic helps to avoid the trades in the overbought (>80) territory. To increase the probability of opening long trades in the direction of a main trend and avoid local bounces we use 200 period EMA.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -3.94%
Maximum Single Profit: +15.78%
Net Profit: +1359.21 USDT (+13.59%)
Total Trades: 111 (36.04% win rate)
Profit Factor: 1.413
Maximum Accumulated Loss: 625.02 USDT (-5.85%)
Average Profit per Trade: 12.25 USDT (+0.40%)
Average Trade Duration: 40 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
MACD with 1D Stochastic Confirmation Reversal StrategyOverview
The MACD with 1D Stochastic Confirmation Reversal Strategy utilizes MACD indicator in conjunction with 1 day timeframe Stochastic indicators to obtain the high probability short-term trend reversal signals. The main idea is to wait until MACD line crosses up it’s signal line, at the same time Stochastic indicator on 1D time frame shall show the uptrend (will be discussed in methodology) and not to be in the oversold territory. Strategy works on time frames from 30 min to 4 hours and opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Higher time frame confirmation: Strategy utilizes 1D Stochastic to establish the major trend and confirm the local reversals with the higher probability.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
MACD line of MACD indicator shall cross over the signal line of MACD indicator.
1D time frame Stochastic’s K line shall be above the D line.
1D time frame Stochastic’s K line value shall be below 80 (not overbought)
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 3.25, value multiplied by ATR to be subtracted from position entry price to setup stop loss)
ATR Trailing Profit Activation Level (by default = 4.25, value multiplied by ATR to be added to position entry price to setup trailing profit activation level)
Trailing EMA Length (by default = 20, period for EMA, when price reached trailing profit activation level EMA will stop out of position if price closes below it)
User can choose the optimal parameters during backtesting on certain price chart, in our example we use default settings.
Justification of Methodology
This strategy leverages 2 time frames analysis to have the high probability reversal setups on lower time frame in the direction of the 1D time frame trend. That’s why it’s recommended to use this strategy on 30 min – 4 hours time frames.
To have an approximation of 1D time frame trend strategy utilizes classical Stochastic indicator. The Stochastic Indicator is a momentum oscillator that compares a security's closing price to its price range over a specific period. It's used to identify overbought and oversold conditions. The indicator ranges from 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions.
It consists of two lines:
%K: The main line, calculated using the formula (CurrentClose−LowestLow)/(HighestHigh−LowestLow)×100 . Highest and lowest price taken for 14 periods.
%D: A smoothed moving average of %K, often used as a signal line.
Strategy logic assumes that on 1D time frame it’s uptrend in %K line is above the %D line. Moreover, we can consider long trade only in %K line is below 80. It means that in overbought state the long trade will not be opened due to higher probability of pullback or even major trend reversal. If these conditions are met we are going to our working (lower) time frame.
On the chosen time frame, we remind you that for correct work of this strategy you shall use 30min – 4h time frames, MACD line shall cross over it’s signal line. The MACD (Moving Average Convergence Divergence) is a popular momentum and trend-following indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in a stock's price.
The MACD consists of three components:
MACD Line: This is the difference between a short-term Exponential Moving Average (EMA) and a long-term EMA, typically calculated as: MACD Line=12-period EMA−26-period
Signal Line: This is a 9-period EMA of the MACD Line, which helps to identify buy or sell signals. When the MACD Line crosses above the Signal Line, it can be a bullish signal (suggesting a buy); when it crosses below, it can be a bearish signal (suggesting a sell).
Histogram: The histogram shows the difference between the MACD Line and the Signal Line, visually representing the momentum of the trend. Positive histogram values indicate increasing bullish momentum, while negative values indicate increasing bearish momentum.
In our script we are interested in only MACD and signal lines. When MACD line crosses signal line there is a high chance that short-term trend reversed to the upside. We use this strategy on 45 min time frame.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -4.79%
Maximum Single Profit: +20.14%
Net Profit: +2361.33 USDT (+44.72%)
Total Trades: 123 (44.72% win rate)
Profit Factor: 1.623
Maximum Accumulated Loss: 695.80 USDT (-5.48%)
Average Profit per Trade: 19.20 USDT (+0.59%)
Average Trade Duration: 30 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe between 30 min and 4 hours and chart (optimal performance observed on 45 min BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Double CCI Confirmed Hull Moving Average Reversal StrategyOverview
The Double CCI Confirmed Hull Moving Average Strategy utilizes hull moving average (HMA) in conjunction with two commodity channel index (CCI) indicators: the slow and fast to increase the probability of entering when the short and mid-term uptrend confirmed. The main idea is to wait until the price breaks the HMA while both CCI are showing that the uptrend has likely been already started. Moreover, strategy uses exponential moving average (EMA) to trail the price when it reaches the specific level. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Double trade setup confirmation: Strategy utilizes two different period CCI indicators to confirm the breakouts of HMA.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
Short-term period CCI indicator shall be above 0.
Long-term period CCI indicator shall be above 0.
Price shall cross the HMA and candle close above it with the same candle
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
CCI Fast Length (by default = 25, used for calculation short term period CCI
CCI Slow Length (by default = 50, used for calculation long term period CCI)
Hull MA Length (by default = 34, period of HMA, which shall be broken to open trade)
Trailing EMA Length (by default = 20)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is CCI and HMA.
The Commodity Channel Index (CCI) is a momentum-based technical indicator used in trading to measure a security's price relative to its average price over a given period. Developed by Donald Lambert in 1980, the CCI is primarily used to identify cyclical trends in a security, helping traders to spot potential buying or selling opportunities.
The CCI formula is:
CCI = (Typical Price − SMA) / (0.015 × Mean Deviation)
Typical Price (TP): This is calculated as the average of the high, low, and closing prices for the period.
Simple Moving Average (SMA): This is the average of the Typical Prices over a specific number of periods.
Mean Deviation: This is the average of the absolute differences between the Typical Price and the SMA.
The result is a value that typically fluctuates between +100 and -100, though it is not bounded and can go higher or lower depending on the price movement.
The Hull Moving Average (HMA) is a type of moving average that was developed by Alan Hull to improve upon the traditional moving averages by reducing lag while maintaining smoothness. The goal of the HMA is to create an indicator that is both quick to respond to price changes and less prone to whipsaws (false signals).
How the Hull Moving Average is Calculated?
The Hull Moving Average is calculated using the following steps:
Weighted Moving Average (WMA): The HMA starts by calculating the Weighted Moving Average (WMA) of the price data over a period square root of n (sqrt(n))
Speed Adjustment: A WMA is then calculated for half of the period n/2, and this is multiplied by 2 to give more weight to recent prices.
Lag Reduction: The WMA of the full period n is subtracted from the doubled n/2 WMA.
Final Smoothing: To smooth the result and reduce noise, a WMA is calculated for the square root of the period n.
The formula can be represented as:
HMA(n) = WMA(WMA(n/2) × 2 − WMA(n), sqrt(n))
The Weighted Moving Average (WMA) is a type of moving average that gives more weight to recent data points, making it more responsive to recent price changes than a Simple Moving Average (SMA). In a WMA, each data point within the selected period is multiplied by a weight, with the most recent data receiving the highest weight. The sum of these weighted values is then divided by the sum of the weights to produce the WMA.
This strategy leverages HMA of user given period as a critical level which shall be broken to say that probability of trend change to the upside increased. HMA reacts faster than EMA or SMA to the price change, that’s why it increases chances to enter new trade earlier. Long-term period CCI helps to have an approximation of mid-term trend. If it’s above 0 the probability of uptrend increases. Short-period CCI allows to have an approximation of short-term trend reversal from down to uptrend. This approach increases chances to have a long trade setup in the direction of mid-term trend when the short-term trend starts to reverse.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements. It’s also important to make a note, that script uses HMA to enter the trade, but for trailing it leverages EMA. It’s used because EMA has no such fast reaction to price move which increases probability not to be stopped out from any significant uptrend move.
Backtest Results
Operating window: Date range of backtests is 2022.07.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 100%
Maximum Single Position Loss: -4.67%
Maximum Single Profit: +19.66%
Net Profit: +14897.94 USDT (+148.98%)
Total Trades: 104 (36.54% win rate)
Profit Factor: 2.312
Maximum Accumulated Loss: 1302.66 USDT (-9.58%)
Average Profit per Trade: 143.25 USDT (+0.96%)
Average Trade Duration: 34 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
LTI_FiltersLinear Time-Invariant (LTI) filters are fundamental tools in signal processing that operate with consistent behavior over time and linearly respond to input signals. They are crucial for analyzing and manipulating signals in various applications, ensuring the output signal's integrity is maintained regardless of when an input is applied or its magnitude. The Windowed Sinc filter is a specific type of LTI filter designed for digital signal processing. It employs a Sinc function, ideal for low-pass filtering, truncated and shaped within a finite window to make it practically implementable. This process involves multiplying the Sinc function by a window function, which tapers off towards the ends, making the filter finite and suitable for digital applications. Windowed Sinc filters are particularly effective for tasks like data smoothing and removing unwanted frequency components, balancing between sharp cutoff characteristics and minimal distortion. The efficiency of Windowed Sinc filters in digital signal processing lies in their adept use of linear algebra, particularly in the convolution process, which combines input data with filter coefficients to produce the desired output. This mathematical foundation allows for precise control over the filtering process, optimizing the balance between filtering performance and computational efficiency. By leveraging linear algebra techniques such as matrix multiplication and Toeplitz matrices, these filters can efficiently handle large datasets and complex filtering tasks, making them invaluable in applications requiring high precision and speed, such as audio processing, financial signal analysis, and image restoration.
Library "LTI_Filters"
offset(length, enable)
Calculates the time offset required for aligning the output of a filter with its input, based on the filter's length. This is useful for centered filters where the output is naturally shifted due to the filter's operation.
Parameters:
length (simple int) : The length of the filter.
enable (simple bool) : A boolean flag to enable or dissable the offset calculation.
Returns: The calculated offset if enabled; otherwise, returns 0.
lti_filter(filter_type, source, length, prefilter, centered, fc, window_type)
General-purpose Linear Time-Invariant (LTI) filter function that can apply various filter types to a data series. Can be used to apply a variety of LTI filters with different characteristics to financial data series or other time series data.
Parameters:
filter_type (simple string) : Specifies the type of filter. ("Sinc", "SMA", "WMA")
source (float) : The input data series to filter.
length (simple int) : The length of the filter.
prefilter (simple bool) : Boolean indicating whether to prefilter the input data.
centered (simple bool) : Determines whether the filter coefficients are centered.
fc (simple float) : Filter cutoff. Expressed like a length.
window_type (simple string) : Type of window function to apply. ("Hann", "Hamming", "Blackman", "Triangular", "Lanczos", "None")
Returns: The filtered data series.
lti_sma(source, length, prefilter)
Applies a Simple Moving Average (SMA) filter to the data series. Useful for smoothing data series to identify trends or for use as a component in more complex indicators.
Parameters:
source (float) : The input data series to filter.
length (simple int) : The length of the SMA filter.
prefilter (simple bool) : Boolean indicating whether to prefilter the input data.
Returns: The SMA-filtered data series.
lti_wma(source, length, prefilter, centered)
Applies a Weighted Moving Average (WMA) filter to a data series. Ideal for smoothing data with emphasis on more recent values, allowing for dynamic adjustments to the weighting scheme.
Parameters:
source (float) : The input data series to filter.
length (simple int) : The length of the WMA filter.
prefilter (simple bool) : Boolean indicating whether to prefilter the input data.
centered (simple bool) : Determines whether the filter coefficients are centered.
Returns: The WMA-filtered data series.
lti_sinc(source, length, prefilter, centered, fc, window_type)
Applies a Sinc filter to a data series, optionally using a window function. Particularly useful for signal processing tasks within financial analysis, such as smoothing or trend identification, with the ability to fine-tune filter characteristics.
Parameters:
source (float) : The input data series to filter.
length (simple int) : The length of the Sinc filter.
prefilter (simple bool) : Boolean indicating whether to prefilter the input data.
centered (simple bool) : Determines whether the filter coefficients are centered.
fc (simple float) : Filter cutoff. Expressed like a length.
window_type (simple string) : Type of window function to apply. ("Hann", "Hamming", "Blackman", "Triangular", "Lanczos", "None")
Returns: The Sinc-filtered data series.
Waindrops [Makit0]█ OVERALL
Plot waindrops (custom volume profiles) on user defined periods, for each period you get high and low, it slices each period in half to get independent vwap, volume profile and the volume traded per price at each half.
It works on intraday charts only, up to 720m (12H). It can plot balanced or unbalanced waindrops, and volume profiles up to 24H sessions.
As example you can setup unbalanced periods to get independent volume profiles for the overnight and cash sessions on the futures market, or 24H periods to get the full session volume profile of EURUSD
The purpose of this indicator is twofold:
1 — from a Chartist point of view, to have an indicator which displays the volume in a more readable way
2 — from a Pine Coder point of view, to have an example of use for two very powerful tools on Pine Script:
• the recently updated drawing limit to 500 (from 50)
• the recently ability to use drawings arrays (lines and labels)
If you are new to Pine Script and you are learning how to code, I hope you read all the code and comments on this indicator, all is designed for you,
the variables and functions names, the sometimes too big explanations, the overall structure of the code, all is intended as an example on how to code
in Pine Script a specific indicator from a very good specification in form of white paper
If you wanna learn Pine Script form scratch just start HERE
In case you have any kind of problem with Pine Script please use some of the awesome resources at our disposal: USRMAN , REFMAN , AWESOMENESS , MAGIC
█ FEATURES
Waindrops are a different way of seeing the volume and price plotted in a chart, its a volume profile indicator where you can see the volume of each price level
plotted as a vertical histogram for each half of a custom period. By default the period is 60 so it plots an independent volume profile each 30m
You can think of each waindrop as an user defined candlestick or bar with four key values:
• high of the period
• low of the period
• left vwap (volume weighted average price of the first half period)
• right vwap (volume weighted average price of the second half period)
The waindrop can have 3 different colors (configurable by the user):
• GREEN: when the right vwap is higher than the left vwap (bullish sentiment )
• RED: when the right vwap is lower than the left vwap (bearish sentiment )
• BLUE: when the right vwap is equal than the left vwap ( neutral sentiment )
KEY FEATURES
• Help menu
• Custom periods
• Central bars
• Left/Right VWAPs
• Custom central bars and vwaps: color and pixels
• Highly configurable volume histogram: execution window, ticks, pixels, color, update frequency and fine tuning the neutral meaning
• Volume labels with custom size and color
• Tracking price dot to be able to see the current price when you hide your default candlesticks or bars
█ SETTINGS
Click here or set any impar period to see the HELP INFO : show the HELP INFO, if it is activated the indicator will not plot
PERIOD SIZE (max 2880 min) : waindrop size in minutes, default 60, max 2880 to allow the first half of a 48H period as a full session volume profile
BARS : show the central and vwap bars, default true
Central bars : show the central bars, default true
VWAP bars : show the left and right vwap bars, default true
Bars pixels : width of the bars in pixels, default 2
Bars color mode : bars color behavior
• BARS : gets the color from the 'Bars color' option on the settings panel
• HISTOGRAM : gets the color from the Bearish/Bullish/Neutral Histogram color options from the settings panel
Bars color : color for the central and vwap bars, default white
HISTOGRAM show the volume histogram, default true
Execution window (x24H) : last 24H periods where the volume funcionality will be plotted, default 5
Ticks per bar (max 50) : width in ticks of each histogram bar, default 2
Updates per period : number of times the histogram will update
• ONE : update at the last bar of the period
• TWO : update at the last bar of each half period
• FOUR : slice the period in 4 quarters and updates at the last bar of each of them
• EACH BAR : updates at the close of each bar
Pixels per bar : width in pixels of each histogram bar, default 4
Neutral Treshold (ticks) : delta in ticks between left and right vwaps to identify a waindrop as neutral, default 0
Bearish Histogram color : histogram color when right vwap is lower than left vwap, default red
Bullish Histogram color : histogram color when right vwap is higher than left vwap, default green
Neutral Histogram color : histogram color when the delta between right and left vwaps is equal or lower than the Neutral treshold, default blue
VOLUME LABELS : show volume labels
Volume labels color : color for the volume labels, default white
Volume Labels size : text size for the volume labels, choose between AUTO, TINY, SMALL, NORMAL or LARGE, default TINY
TRACK PRICE : show a yellow ball tracking the last price, default true
█ LIMITS
This indicator only works on intraday charts (minutes only) up to 12H (720m), the lower chart timeframe you can use is 1m
This indicator needs price, time and volume to work, it will not work on an index (there is no volume), the execution will not be allowed
The histogram (volume profile) can be plotted on 24H sessions as limit but you can plot several 24H sessions
█ ERRORS AND PERFORMANCE
Depending on the choosed settings, the script performance will be highly affected and it will experience errors
Two of the more common errors it can throw are:
• Calculation takes too long to execute
• Loop takes too long
The indicator performance is highly related to the underlying volatility (tick wise), the script takes each candlestick or bar and for each tick in it stores the price and volume, if the ticker in your chart has thousands and thousands of ticks per bar the indicator will throw an error for sure, it can not calculate in time such amount of ticks.
What all of that means? Simply put, this will throw error on the BITCOIN pair BTCUSD (high volatility with tick size 0.01) because it has too many ticks per bar, but lucky you it will work just fine on the futures contract BTC1! (tick size 5) because it has a lot less ticks per bar
There are some options you can fine tune to boost the script performance, the more demanding option in terms of resources consumption is Updates per period , by default is maxed out so lowering this setting will improve the performance in a high way.
If you wanna know more about how to improve the script performance, read the HELP INFO accessible from the settings panel
█ HOW-TO SETUP
The basic parameters to adjust are Period size , Ticks per bar and Pixels per bar
• Period size is the main setting, defines the waindrop size, to get a better looking histogram set bigger period and smaller chart timeframe
• Ticks per bar is the tricky one, adjust it differently for each underlying (ticker) volatility wise, for some you will need a low value, for others a high one.
To get a more accurate histogram set it as lower as you can (min value is 1)
• Pixels per bar allows you to adjust the width of each histogram bar, with it you can adjust the blank space between them or allow overlaping
You must play with these three parameters until you obtain the desired histogram: smoother, sharper, etc...
These are some of the different kind of charts you can setup thru the settings:
• Balanced Waindrops (default): charts with waindrops where the two halfs are of same size.
This is the default chart, just select a period (30m, 60m, 120m, 240m, pick your poison), adjust the histogram ticks and pixels and watch
• Unbalanced Waindrops: chart with waindrops where the two halfs are of different sizes.
Do you trade futures and want to plot a waindrop with the first half for the overnight session and the second half for the cash session? you got it;
just adjust the period to 1860 for any CME ticker (like ES1! for example) adjust the histogram ticks and pixels and watch
• Full Session Volume Profile: chart with waindrops where only the first half plots.
Do you use Volume profile to analize the market? Lucky you, now you can trick this one to plot it, just try a period of 780 on SPY, 2760 on ES1!, or 2880 on EURUSD
remember to adjust the histogram ticks and pixels for each underlying
• Only Bars: charts with only central and vwap bars plotted, simply deactivate the histogram and volume labels
• Only Histogram: charts with only the histogram plotted (volume profile charts), simply deactivate the bars and volume labels
• Only Volume: charts with only the raw volume numbers plotted, simply deactivate the bars and histogram
If you wanna know more about custom full session periods for different asset classes, read the HELP INFO accessible from the settings panel
EXAMPLES
Full Session Volume Profile on MES 5m chart:
Full Session Unbalanced Waindrop on MNQ 2m chart (left side Overnight session, right side Cash Session):
The following examples will have the exact same charts but on four different tickers representing a futures contract, a forex pair, an etf and a stock.
We are doing this to be able to see the different parameters we need for plotting the same kind of chart on different assets
The chart composition is as follows:
• Left side: Volume Labels chart (period 10)
• Upper Right side: Waindrops (period 60)
• Lower Right side: Full Session Volume Profile
The first example will specify the main parameters, the rest of the charts will have only the differences
MES :
• Left: Period size: 10, Bars: uncheck, Histogram: uncheck, Execution window: 1, Ticks per bar: 2, Updates per period: EACH BAR,
Pixels per bar: 4, Volume labels: check, Track price: check
• Upper Right: Period size: 60, Bars: check, Bars color mode: HISTOGRAM, Histogram: check, Execution window: 2, Ticks per bar: 2,
Updates per period: EACH BAR, Pixels per bar: 4, Volume labels: uncheck, Track price: check
• Lower Right: Period size: 2760, Bars: uncheck, Histogram: check, Execution window: 1, Ticks per bar: 1, Updates per period: EACH BAR,
Pixels per bar: 2, Volume labels: uncheck, Track price: check
EURUSD :
• Upper Right: Ticks per bar: 10
• Lower Right: Period size: 2880, Ticks per bar: 1, Pixels per bar: 1
SPY :
• Left: Ticks per bar: 3
• Upper Right: Ticks per bar: 5, Pixels per bar: 3
• Lower Right: Period size: 780, Ticks per bar: 2, Pixels per bar: 2
AAPL :
• Left: Ticks per bar: 2
• Upper Right: Ticks per bar: 6, Pixels per bar: 3
• Lower Right: Period size: 780, Ticks per bar: 1, Pixels per bar: 2
█ THANKS TO
PineCoders for all they do, all the tools and help they provide and their involvement in making a better community
scarf for the idea of coding a waindrops like indicator, I did not know something like that existed at all
All the Pine Coders, Pine Pros and Pine Wizards, people who share their work and knowledge for the sake of it and helping others, I'm very grateful indeed
I'm learning at each step of the way from you all, thanks for this awesome community;
Opensource and shared knowledge: this is the way! (said with canned voice from inside my helmet :D)
█ NOTE
This description was formatted following THIS guidelines
═════════════════════════════════════════════════════════════════════════
I sincerely hope you enjoy reading and using this work as much as I enjoyed developing it :D
GOOD LUCK AND HAPPY TRADING!
ATR Extension from Moving Average, with Robust Sigma Bands
# ATR Extension from Moving Average, with Robust Sigma Bands
**What it does**
This indicator measures how far price is from a selected moving average, expressed in **ATR multiples**, then overlays **robust sigma bands** around the long run central tendency of that extension. Positive values mean price is extended above the MA, negative values mean price is extended below the MA. The signal adapts to volatility through ATR, which makes comparisons consistent across symbols and regimes.
**Why it can help**
* Normalizes distance to an MA by ATR, which controls for changing volatility
* Uses the **bar’s extreme** against the MA, not just the close, so it captures true stretch
* Computes a **median** and **standard deviation** of the extension over a multi-year window, which yields simple, intuitive bands for trend and mean-reversion decisions
---
## Inputs
* **MA length**: default 50, options 200, 64, 50, 20, 9, 4, 3
* **MA timeframe**: Daily or Weekly. The MA is computed on the chosen higher timeframe through `request.security`.
* **MA type**: EMA or SMA
* **Years lookback**: 1 to 10 years, default 5. This sets the sample for the median and sigma calculation, `years * 365` bars.
* **Line width**: visual width of the plotted extension series
* **Table**: optional on-chart table that displays the current long run **median** and **sigma** of the extension, with selectable text size
**Fixed parameters in this release**
* **ATR length**: 20 on the daily timeframe
* **ATR type**: classic ATR. ADR percent is not enabled in this version.
---
## Plots and colors
* **Main plot**: “Extension from 50d EMA” by default. Value is in **ATR multiples**.
* **Reference lines**:
* `median` line, black dashed
* +2σ orange, +3σ red
* −2σ blue, −3σ green
---
## How it is calculated
1. **Moving average** on the selected higher timeframe: EMA or SMA of `close`.
2. **Extreme-based distance** from MA, as a percent of price:
* If `close > MA`, use `(high − MA) / close * 100`
* Else, use `(low − MA) / close * 100`
3. **ATR percent** on the daily timeframe: `ATR(20) / close * 100`
4. **ATR multiples**: extension percent divided by ATR percent
5. **Robust center and spread** over the chosen lookback window:
* Center: **median** of the ATR-multiple series
* Spread: **standard deviation** of that series
* Bands: center ± 1σ, 2σ, 3σ, with 2σ and 3σ drawn
This design yields an intuitive unit scale. A value of **+2.0** means price is about 2 ATR above the selected MA by the most stretched side of the current bar. A value of **−3.0** means roughly 3 ATR below.
---
## Practical use
* **Trend continuation**
* Sustained readings near or above **+1σ** together with a rising MA often signal healthy momentum.
* **Mean reversion**
* Spikes into **±2σ** or **±3σ** can identify stretched conditions for fade setups in range or late-trend environments.
* **Regime awareness**
* The **median** moves slowly. When median drifts positive for many months, the market spends more time extended above the MA, which often marks bullish regimes. The opposite applies in bearish regimes.
**Notes**
* The MA can be set to Weekly while ATR remains Daily. This is deliberate, it keeps the normalization stable for most symbols.
* On very short intraday charts, the extension remains meaningful since it references the session’s extreme against a higher-timeframe MA and a daily ATR.
* Symbols with short histories may not fill the lookback window. Bands will adapt as data accrues.
---
## Table overlay
Enable **Table → Show** to see:
* “ATR from \”
* Current **median** and **sigma** of the extension series for your lookback
---
## Recommended settings
* **Swing equities**: 50 EMA on Daily, 5 to 7 years
* **Index trend work**: 200 EMA on Daily, 10 years
* **Position trading**: 20 or 50 EMA on Weekly MA, 5 to 10 years
---
## Interpretation examples
* Reading **+2.7** with price above a rising 50 EMA, near prior highs
* Strong trend extension, consider pyramiding in trend systems or waiting for a pullback if you are a mean-reverter.
* Reading **−2.2** into multi-month support with flattening MA
* Stretch to the downside that often mean-reverts, size entries based on your system rules.
---
## Credits
The concept of measuring stretch from a moving average in ATR units has a rich community history. This implementation and its presentation draw on ideas popularized by **Jeff Sun**, **SugarTrader**, and **Steve D Jacobs**. Thanks to each for their contributions to ATR-based extension thinking.
---
## License
This script and description are distributed under **MPL-2.0**, consistent with the header in the source code.
---
## Changelog
* **v1.0**: Initial public release. Daily ATR normalization, EMA or SMA on D or W timeframe, robust median and sigma bands, optional table.
---
## Disclaimer
This tool is for educational use only. It is not financial advice. Always test on your own data and strategies, then manage risk accordingly.
FIR Low Pass Filter Suite (FIR)The FIR Low Pass Filter Suite is an advanced signal processing indicator that applies finite impulse response (FIR) filtering techniques to price data. At its core, the indicator uses windowed-sinc filtering, which provides optimal frequency response characteristics for separating trend from noise in financial data.
The indicator offers multiple window functions including Kaiser, Kaiser-Bessel Derived (KBD), Hann, Hamming, Blackman, Triangular, and Lanczos. Each window type provides different trade-offs between main-lobe width and side-lobe attenuation, allowing users to fine-tune the frequency response characteristics of the filter. The Kaiser and KBD windows provide additional control through an alpha parameter that adjusts the shape of the window function.
A key feature is the ability to operate in either linear or logarithmic space. Logarithmic filtering can be particularly appropriate for financial data due to the multiplicative nature of price movements. The indicator includes an envelope system that can adaptively calculate bands around the filtered price using either arithmetic or geometric deviation, with separate controls for upper and lower bands to account for the asymmetric nature of market movements.
The implementation handles edge effects through proper initialization and offers both centered and forward-only filtering modes. Centered mode provides zero phase distortion but introduces lag, while forward-only mode operates causally with no lag but introduces some phase distortion. All calculations are performed using vectorized operations for efficiency, with carefully designed state management to handle the filter's warm-up period.
Visual feedback is provided through customizable color gradients that can reflect the current trend direction, with optional glow effects and background fills to enhance visibility. The indicator maintains high numerical precision throughout its calculations while providing smooth, artifact-free output suitable for both analysis and visualization.
