EMA 20The EMA 20 (Exponential Moving Average 20) is a simple trend-following indicator designed to smooth price fluctuations and highlight short-term market direction.
This script plots a 20-period exponential moving average in red, allowing traders to quickly assess whether price is trading above or below the short-term trend.
When price remains above the EMA 20, it often suggests bullish strength; when price falls below it, it may indicate short-term weakness.
This indicator is minimal, clear, and useful as a foundational trend reference in any trading system.
المتوسطات المتحركة
Single AHR DCA (HM) — AHR Pane (customized quantile)Customized note
The log-regression window LR length controls how long a long-term fair value path is estimated from historical data.
The AHR window AHR window length controls over which historical regime you measure whether the coin is “cheap / expensive”.
When you choose a log-regression window of length L (years) and an AHR window of length A (years), you can intuitively read the indicator as:
“Within the last A years of this regime, relative to the long-term trend estimated over the same A years, the current price is cheap / neutral / expensive.”
Guidelines:
In general, set the AHR window equal to or slightly longer than the LR window:
If the AHR window is much longer than LR, you mix different baselines (different LR regimes) into one distribution.
If the AHR window is much shorter than LR, quantiles mostly reflect a very local slice of history.
For BTC / ETH and other BTC-like assets, you can use relatively long horizons (e.g. LR ≈ 3–5 years, AHR window ≈ 3–8 years).
For major altcoins (BNB / SOL / XRP and similar high-beta assets), it is recommended to use equal or slightly shorter horizons, e.g. LR ≈ 2–3 years, AHR window ≈ 2–3 years.
1. Price series & windows
Working timeframe: daily (1D).
Let the daily close of the current symbol on day t be P_t .
Main length parameters:
HM window: L_HM = maLen (default 200 days)
Log-regression window: L_LR = lrLen (default 1095 days ≈ 3 years)
AHR window (regime window): W = windowLen (default 1095 days ≈ 3 years)
2. Harmonic moving average (HM)
On a window of length L_HM, define the harmonic mean:
HM_t = ^(-1)
Here eps = 1e-10 is used to avoid division by zero.
Intuition: HM is more sensitive to low prices – an extremely low price inside the window will drag HM down significantly.
3. Log-regression baseline (LR)
On a window of length L_LR, perform a linear regression on log price:
Over the last L_LR bars, build the series
x_k = log( max(P_k, eps) ), for k = t-L_LR+1 ... t, and fit
x_k ≈ a + b * k.
The fitted value at the current index t is
log_P_hat_t = a + b * t.
Exponentiate to get the log-regression baseline:
LR_t = exp( log_P_hat_t ).
Interpretation: LR_t is the long-term trend / fair value path of the current regime over the past L_LR days.
4. HM-based AHR (valuation ratio)
At each time t, build an HM-based AHR (valuation multiple):
AHR_t = ( P_t / HM_t ) * ( P_t / LR_t )
Interpretation:
P_t / HM_t : deviation of price from the mid-term HM (e.g. 200-day harmonic mean).
P_t / LR_t : deviation of price from the long-term log-regression trend.
Multiplying them means:
if price is above both HM and LR, “expensiveness” is amplified;
if price is below both, “cheapness” is amplified.
Typical reading:
AHR_t < 1 : price is below both mid-term mean and long-term trend → statistically cheaper.
AHR_t > 1 : price is above both mid-term mean and long-term trend → statistically more expensive.
5. Empirical quantile thresholds (Opp / Risk)
On each new day, whenever AHR_t is valid, add it into a rolling array:
A_t_window = { AHR_{t-W+1}, ..., AHR_t } (at most W = windowLen elements)
On this empirical distribution, define two quantiles:
Opportunity quantile: q_opp (default 15%)
Risk quantile: q_risk (default 65%)
Using standard percentile computation (order statistics + linear interpolation), we get:
Opp threshold:
theta_opp = Percentile( A_t_window, q_opp )
Risk threshold:
theta_risk = Percentile( A_t_window, q_risk )
We also compute the percentile rank of the current AHR inside the same history:
q_now = PercentileRank( A_t_window, AHR_t ) ∈
This yields three valuation zones:
Opportunity zone: AHR_t <= theta_opp
(corresponds to roughly the cheapest ~q_opp% of historical states in the last W days.)
Neutral zone: theta_opp < AHR_t < theta_risk
Risk zone: AHR_t >= theta_risk
(corresponds to roughly the most expensive ~(100 - q_risk)% of historical states in the last W days.)
All quantiles are purely empirical and symbol-specific: they are computed only from the current asset’s own history, without reusing BTC thresholds or assuming cross-asset similarity.
6. DCA simulation (lightweight, rolling window)
Given:
a daily budget B (input: budgetPerDay), and
a DCA simulation window H (input: dcaWindowLen, default 900 days ≈ 2.5 years),
The script applies the following rule on each new day t:
If thresholds are unavailable or AHR_t > theta_risk
→ classify as Risk zone → buy = 0
If AHR_t <= theta_opp
→ classify as Opportunity zone → buy = 2B (double size)
Otherwise (Neutral zone)
→ buy = B (normal DCA)
Daily invested cash:
C_t ∈ {0, B, 2B}
Daily bought quantity:
DeltaQ_t = C_t / P_t
The script keeps rolling sums over the last H days:
Cumulative position:
Q_H = sum_{k=t-H+1..t} DeltaQ_k
Cumulative invested cash:
C_H = sum_{k=t-H+1..t} C_k
Current portfolio value:
PortVal_t = Q_H * P_t
Cumulative P&L:
PnL_t = PortVal_t - C_H
Active days:
number of days in the last H with C_k > 0.
These results are only used to visualize how this AHR-quantile-driven DCA rule would have behaved over the recent regime, and do not constitute financial advice.
Buy Sell Signal — Ema crossover [© gyanapravah_odisha]Professional EMA Crossover + ATR Risk Control
Trade with confidence using a complete system that gives you clear entries, smart exits, and full automation.
Includes:
Precision 5/13 EMA crossover signals
ATR-based adaptive stop-loss
Multiple take-profit levels (with intermediate targets)
Fully customizable R:R ratios
ATR + volume filters to avoid choppy markets
Real-time trade dashboard
All alerts included
Built for: Crypto, Forex, Stocks • Scalping & Swing Trading
Built for you: Free, open-source & made for real-world trading.
VWAP & EMA9 Cross AlertAlerts the user when VWAP and EMA 9 cross. It gives a general direction of the market to help make decisions.
VWAP & EMA9 Cross AlertAlerts when EMA9 and VWAP Cross. This provides an indicator of general market direction based on these 2 indicators.
Estrategia TEMA Pro [Límite Ops por Ventana]good money
jucale
keep faith
dont stop working
eat vegatables
Mebane Faber GTAA 5In 2007, Mebane Faber published research that challenged the conventional wisdom of buy-and-hold investing. His paper, titled "A Quantitative Approach to Tactical Asset Allocation" and published in the Journal of Wealth Management, demonstrated that a simple timing mechanism could reduce portfolio volatility and drawdowns while maintaining competitive returns (Faber, 2007). This indicator implements his Global Tactical Asset Allocation strategy, known as GTAA5, following the original methodology.
The core insight of Faber's research stems from a century of market data. By analyzing asset class performance from 1901 onwards, Faber found that a ten-month simple moving average served as an effective trend filter across major asset classes. When an asset trades above its ten-month moving average, it tends to continue its upward trajectory; when it falls below, significant drawdowns often follow (Faber, 2007, pp. 12-16). This observation aligns with momentum research by Jegadeesh and Titman (1993), who documented that intermediate-term momentum persists across equity markets.
The GTAA5 strategy allocates capital equally across five diversified asset classes: domestic equities (SPY), international developed markets (EFA), aggregate bonds (AGG), commodities (DBC), and real estate investment trusts (VNQ). Each asset receives a twenty percent allocation when trading above its ten-month moving average. When an asset falls below this threshold, its allocation moves to short-term treasury bills (SHY), creating a dynamic cash position that scales with market risk (Cambria Investment Management, 2013).
The strategy's historical performance during market crises illustrates its function. During the 2008 financial crisis, traditional sixty-forty portfolios experienced drawdowns exceeding forty percent. The GTAA5 strategy limited losses to approximately twelve percent by reducing equity exposure as prices declined below their moving averages (Faber, 2013). This asymmetric return profile represents the strategy's primary characteristic.
This implementation uses monthly closing prices retrieved via request.security() to calculate the ten-month simple moving average. This distinction matters, as approximations using daily data (such as a 200-day moving average) can generate different signals during volatile periods. Monthly data ensures the indicator produces signals consistent with published academic research.
The indicator provides position monitoring, automatic rebalancing detection on either the first or last trading day of each month, and share calculations based on user-defined capital. A dashboard displays current trend status for each asset class, target versus actual weightings, and trade instructions for rebalancing. Performance metrics including annualized volatility and Sharpe ratio provide ongoing risk assessment.
Several limitations warrant acknowledgment. First, the strategy rebalances monthly, meaning it cannot respond to intra-month market crashes. Second, transaction costs and taxes from monthly rebalancing may reduce net returns for taxable accounts. Third, the ten-month lookback period, while historically robust, offers no guarantee of future effectiveness. As Ilmanen (2011) notes in "Expected Returns", all timing strategies face the risk of regime change, where historical relationships break down.
This indicator serves educational purposes and portfolio monitoring. It does not constitute financial advice.
References:
Cambria Investment Management (2013). Global Tactical Asset Allocation: An Introduction to the Approach. Research Report, Los Angeles.
Faber, M.T. (2007). A Quantitative Approach to Tactical Asset Allocation. Journal of Wealth Management, Spring 2007, pp. 9-79.
Faber, M.T. (2013). Global Asset Allocation: A Survey of the World's Top Asset Allocation Strategies. Cambria Investment Management, Los Angeles.
Ilmanen, A. (2011). Expected Returns: An Investor's Guide to Harvesting Market Rewards. John Wiley and Sons, Chichester.
Jegadeesh, N. and Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), pp. 65-91.
2t's MA 50, MA 150, ATRThis indicator displays three key technical signals on the chart:
SMA 50 – Short-term trend direction
SMA 150 – Medium-term trend direction
ATR – Market volatility (Average True Range)
Line colors and lengths can be customized in the settings.
The ATR is plotted on the same chart for quick volatility reference without needing a separate panel.
This tool is designed for traders who want a clean, lightweight view of trend strength and volatility in a single indicator.
inyerneck Diaper Sniper v16 — LOW VOL V CATCHERDiaper Sniper v16 — Low-Vol Reversal Hunter
Catches dead-cat bounces and V-shaped reversals on the day’s biggest losers.
Designed for pennies and trash stocks that drop 6 %+ from recent high and snap back on any volume + green candle.
Features:
• Tiny green “D” = reversal signal
• Works on 1m → daily
• Fully adjustable filters
Best on low-float runners that bleed hard and bounce harder.
Use tiny size — it fires a lot.
Public version — code visible. No invite-only on Essential plan.
do not alter settings with out first recording defaults.. defaults are quite effective
2025 build. Test at your own risk.
Support Line [by rukich]🟠 OVERVIEW
The indicator displays a floating line that acts as a support level. It's important to remember that any support level can be broken.
🟠 COMPONENTS
The indicator is based on the percentage difference between the closes of the n-th bar back and the current bar. The resulting percentage is smoothed to remove noise.
The indicator is displayed as a green-red line (the colors don’t carry meaning — they are used just for visual variety). When the price touches the support level, the bar background turns green.
For convenience, there is a label on the right side of the indicator showing the current value of the line.
🟠 HOW TO USE
The indicator includes several settings that can be adjusted, though optimal defaults are provided.
Settings:
Timeframe — specifies which timeframe’s data is used to calculate the line.
Candles back — specifies how many bars back from the current one are used.
The indicator should be used according to general support-zone logic. Since no support zone guarantees a price bounce, the optimal approach is to confirm the reaction after the price touches the line.
Example of use:
In the current example, the Timeframe in the indicator settings is set to 1 hour, and the currently open chart is 5 minutes. This means that on the 5-minute chart we see a 1-hour line. After the price touches the support line, you need to see a confirmation of the reaction to understand whether the support zone is holding the price.
In the examples, reaction confirmation is shown through: the formation of an M5 shift and the invalidation of an FVG M5- (the latter is more risky than the M5 shift):
🟠 CONCLUSION
The indicator shows a floating support zone, and when tested, you should confirm the reaction on a lower timeframe.
Classic Wave: The Easy WayClassic Wave is a simple strategy with few rules and no over-optimization. Despite its simplicity, it is backed by a nearly century-long historical track record, delivering excellent returns on the weekly chart of the SPX (TVC).
I also recommend observing its strong performance on the SPY (weekly), which is the perfect instrument for executing this strategy with futures in the future.
Strategy Rules and Parameters
When a bullish candle closes above the 20-period EMA, we place the stop-loss below the low of that candle and target a risk-reward ratio of 1:1.
A second, more profitable variant is to change the risk-reward ratio in the code to 2:1.
-Total capital: $10,000
-We use 10% of the total capital per trade.
-Commissions: 0.1% per trade.
The code construction is simple and very well detailed within the script itself.
Risk-Reward Ratio 2:1
Using a 2:1 risk-reward ratio reduces the win rate but significantly increases profitability.
Across the full historical data of the SPX index (weekly), the system would have generated 236 trades, with a win rate of 51.27% and a profit factor of 2.53.
From January 1, 2023, to November 28, 2025, the system would have generated 5 trades, with an 80% win rate and a profit factor of 9.244.
What makes this system so good?
-It takes advantage of the long-term bullish bias of U.S. stock indices and traditional markets.
-It filters out a lot of noise thanks to the weekly timeframe.
-It uses simple parameters with no over-optimization.
Final Notes:
This strategy has consistently outperformed the returns offered by most traditional funds over time, with fewer drawdowns and significantly less stress. I hope you like it.
Trend Breakout & Ratchet Stop System [Market Filter]Description:
This strategy implements a robust trend-following system designed to capture momentum moves while strictly managing downside risk through a multi-stage "Ratchet" exit mechanism and broad market filters.
It is designed for swing traders who want to align individual stock entries with the overall market direction.
How it works:
1. Market Regime Filters (The "Safety Check") Before taking any position, the strategy checks the health of the broader market to avoid "catching falling knives."
Broad Market Filter: By default, it checks NASDAQ:QQQ (adjustable). If the benchmark is trading below its SMA 200, the strategy assumes a Bear Market and suppresses all new long entries.
Volatility Filter (VIX): Uses CBOE:VIX to gauge fear. If the VIX is above a specific threshold (Default: 32), entries are paused, and existing positions can optionally be closed to preserve capital.
2. Entry Logic Entries are based on Momentum and Trend confirmation. A position is opened if filters are clear AND one of the following occurs:
Golden Cross: SMA 25 crosses over SMA 50.
SMA Breakouts: A "Three-Bar-Break" logic confirms a breakout above the SMA 50, 100, or 200 (price must establish itself above the moving average).
3. The "Ratchet" Exit System The exit logic evolves as the trade progresses, tightening risk like a ratchet:
Stage 0 (Initial Risk): Starts with a standard percentage Stop Loss from the entry price.
Stage 1 (Breakeven/Lock): Once the price rises by Profit Step 1 (e.g., +10%), the Stop Loss jumps to a tighter level and locks there. This secures the initial move.
Stage 2 (Trailing Mode): If the price continues to rise to Profit Step 2 (e.g., +15%), the Stop Loss converts into a dynamic Trailing Stop relative to the Highest High. This allows the trade to run as long as the trend persists.
Additional Exits:
Dead Cross: Closes position if SMA 25 crosses under SMA 50.
VIX Panic: Emergency exit if volatility spikes above the threshold.
Settings & Customization:
SMAs: Adjustable lengths for all Moving Averages.
Filters: Toggle Market/VIX filters on/off and choose your benchmark ticker (e.g., SPY or QQQ).
Risk Management: Fully customizable percentages for the Ratchet steps (Initial SL, Stage 1 Trigger, Trailing distance).
Dynamic Ratchet Trend Strategy [VIX Filter]Overview This strategy is a long-only trend-following system designed to capture major market moves while strictly managing downside risk through a state-machine based "Ratchet" exit logic. It incorporates a volatility filter using the CBOE VIX index to stay out of (or exit) the market during high-stress environments.
Key Features
1. Multi-Condition Entries The strategy looks for momentum shifts and trend breakouts using four Simple Moving Averages (25, 50, 100, 200).
Momentum Cross: SMA 25 crossover above SMA 50.
Trend Breakouts: A specific "3-Bar Breakout" logic above the SMA 50, 100, or 200. This requires the price to hold above the SMA for 3 consecutive bars after being below it, reducing false signals compared to simple closes.
2. VIX Volatility Filter Before entering any trade, the script checks the CBOE:VIX.
Filter: If VIX is above the threshold (default 32), new entries are blocked.
Panic Exit: If you are in a position and the VIX spikes above the threshold, the strategy executes an immediate "Panic Exit" to preserve capital during market crashes.
3. The "Ratchet" Exit System (3 Stages) Unlike a standard trailing stop, this strategy uses a 3-stage dynamic exit mechanism that tightens as profits grow:
Stage 0 (Initial Risk): Standard percentage-based Stop Loss from the entry price.
Stage 1 (The Lock-In): Triggered when profit hits 10% (configurable).
Unique Logic: Instead of trailing from the highest high, the stop is calculated based on the price at the exact moment this stage was triggered. It "steps up" once and holds, securing the initial move without being prematurely stopped out by normal volatility.
Stage 2 (Trailing Mode): Triggered when profit hits 15% (configurable).
The strategy switches to a classic Trailing Stop, following the percentage distance from the Highest High.
4. Emergency Backup A "Dead Cross" (SMA 25 crossing under SMA 50) acts as a final fail-safe to close positions if the trend reverses completely before hitting a stop.
Settings & Inputs
SMAs: Customize the lengths for all four moving averages.
VIX Filter: Toggle the filter on/off and set the panic threshold.
Exit Logic: Fully customizable percentages for Initial SL, Stage 1 Trigger/Distance, and Stage 2 Trigger/Trailing Distance.
Disclaimer This script is for educational purposes only. Past performance is not indicative of future results. Always manage your risk appropriately.
Moving Average 13 Exponential//@version=6
indicator(title="Moving Average 13 Exponential", shorttitle="EMA", overlay=true, timeframe="", timeframe_gaps=true)
len = input.int(9, minval=1, title="Length")
src = input(close, title="Source")
offset = input.int(title="Offset", defval=0, minval=-500, maxval=500, display = display.data_window)
out = ta.ema(src, len)
plot(out, title="EMA", color=color.yellow, offset=offset)
// Smoothing MA inputs
GRP = "Smoothing"
TT_BB = "Only applies when 'SMA + Bollinger Bands' is selected. Determines the distance between the SMA and the bands."
maTypeInput = input.string("None", "Type", options = , group = GRP, display = display.data_window)
var isBB = maTypeInput == "SMA + Bollinger Bands"
maLengthInput = input.int(14, "Length", group = GRP, display = display.data_window, active = maTypeInput != "None")
bbMultInput = input.float(2.0, "BB StdDev", minval = 0.001, maxval = 50, step = 0.5, tooltip = TT_BB, group = GRP, display = display.data_window, active = isBB)
var enableMA = maTypeInput != "None"
// Smoothing MA Calculation
ma(source, length, MAtype) =>
switch MAtype
"SMA" => ta.sma(source, length)
"SMA + Bollinger Bands" => ta.sma(source, length)
"EMA" => ta.ema(source, length)
"SMMA (RMA)" => ta.rma(source, length)
"WMA" => ta.wma(source, length)
"VWMA" => ta.vwma(source, length)
// Smoothing MA plots
smoothingMA = enableMA ? ma(out, maLengthInput, maTypeInput) : na
smoothingStDev = isBB ? ta.stdev(out, maLengthInput) * bbMultInput : na
plot(smoothingMA, "EMA-based MA", color=color.yellow, display = enableMA ? display.all : display.none, editable = enableMA)
bbUpperBand = plot(smoothingMA + smoothingStDev, title = "Upper Bollinger Band", color=color.green, display = isBB ? display.all : display.none, editable = isBB)
bbLowerBand = plot(smoothingMA - smoothingStDev, title = "Lower Bollinger Band", color=color.green, display = isBB ? display.all : display.none, editable = isBB)
fill(bbUpperBand, bbLowerBand, color= isBB ? color.new(color.green, 90) : na, title="Bollinger Bands Background Fill", display = isBB ? display.all : display.none, editable = isBB)
Z-EMA Fusion BandsDesigned with crypto markets in mind, particularly Bitcoin , it builds on the concept that the 1-Week 50 EMA often serves as a long-term bull/bear market threshold — an area where institutional bias, momentum shifts, and cyclical rotations tend to occur.
🔹 Core Components & Synergies:
1. 1W 50 EMA (Higher Timeframe)
- This EMA is calculated on a weekly timeframe, regardless of your current chart.
- In crypto, price above the 1W 50 EMA typically aligns with long-term bull market phases, while extended periods below can signify bearish macro structure.
- The slope of the EMA is also analyzed to add directional confidence to trend strength.
2. ±1 Standard Deviation Bands
- Surrounding the 50 EMA, these bands visualize normal price dispersion relative to trend.
- When price consistently hugs or breaks outside these bands, it often reflects market expansion, volatility events, or mean-reversion opportunity.
3. Z-Score Gradient Fill
- The area between the bands is filled using a Z-score-based gradient, which dynamically adjusts color based on how far price is from the EMA (in terms of standard deviations).
- Color shifts from aqua (near EMA) to fuchsia (far from EMA) help you spot price compression, equilibrium, or overextension at a glance.
- The fill also uses transparency scaling, making it fade as price stretches further, emphasizing the core structure.
4. Directional EMA Coloring
- The EMA line itself is colored based on:
- The slope of the EMA (rising/falling)
- Whether the HTF candle is bullish or bearish
- This provides intuitive color-coded confirmation of momentum alignment or potential exhaustion.
5. Price/EMA Divergence Detection
- The script detects bullish and bearish divergence between price and the EMA (rather than using a traditional oscillator).
- Bullish Divergence: Price makes a lower low, EMA makes a higher low.
- Bearish Divergence: Price makes a higher high, EMA makes a lower high.
- These signals often mark transitional zones where momentum fades before a trend reversal or correction.
📊 Suggested Uses:
🔸 Swing and Position Trading:
- Use the 1W 50 EMA as a macro-trend anchor.
- Stay long-biased when price is above with positive slope, and short-biased when below.
- Consider entries near band edges for mean-reversion plays, especially if confluence forms with divergence signals.
🔸 Volatility-Based Filtering:
- Use the Z-score fill to identify volatility compression (near EMA) or expansion (edge of bands).
- Combine this with breakout strategies or dynamic position sizing.
🔸 Divergence Confirmation:
- Combine divergence markers with HTF EMA slope for high-probability setups.
- Bullish div + EMA flattening/rising can signal the start of accumulation after a macro dip.
🔸 Multi-Timeframe Analysis:
- Works well as a structural overlay on intraday charts (1H, 4H, 1D).
- Use this indicator to track long-term bias while executing lower timeframe trades.
⚠️ Disclaimer:
This indicator is designed for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset.
Always use proper risk management, and combine with your own analysis, tools, and strategy. Performance in past market conditions does not guarantee future results.
Simple Price ChannelSimple Price Channel
This indicator plots a basic volatility-based channel around a moving average.
Features:
Midline using Simple Moving Average (SMA)
Upper & lower bands using ATR or true range
Channel fill for easy trend visualisation
This script is designed for educational and analytical purposes only.
It does not provide signals, alerts, or financial advice.
4H EMA 21/30 Cloud on 15mThis indicator displays the 4-hour EMA 21 and EMA 30 as a dynamic cloud directly on the 15-minute chart, providing a clean and reliable higher-timeframe trend filter for intraday and scalping setups.
The cloud turns:
Green when EMA21 > EMA30 → bullish HTF trend
Red when EMA21 < EMA30 → bearish HTF trend
Because the 4H EMA 21/30 combination tracks mid-term momentum and trend structure extremely well, this indicator helps traders avoid counter-trend trades, time pullbacks more effectively, and align entries with dominant higher-timeframe flow.
Perfect for traders using:
Price Action
FVG / Imbalance concepts
CHOCH/BOS structure
Liquidity-based models
ICT-style intraday execution
Use the 4H cloud as your HTF bias anchor, and execute trades using your own entry model on the 15m timeframe.
CDC Action Zone V.2 strategy — Updated v6Making a profit with a candlestick structure compared to the MA course 25 line with nine intersecting to find. Buy in the market.
APEX TREND: Macro & Hard Stop SystemAPEX TREND: Macro & Hard Stop System
The APEX TREND System is a composite trend-following strategy engineered to solve the "Whipsaw" problem inherent in standard breakout systems. It orchestrates four distinct technical theories—Macro Trend Filtering, Volatility Squeeze, Momentum, and Volatility Stop-Loss—into a single, hierarchical decision-making engine.
This script is not merely a collection of indicators; it is a rules-based trading system designed for Swing Traders (Day/Week timeframes) who aim to capture major trend extensions while strictly managing downside risk through a "Hard Stop" mechanism.
🧠 Underlying Concepts & Originality
Many trend indicators fail because they treat all price movements equally. The APEX TREND differentiates itself by applying an "Institutional Filter" logic derived from classic Dow Theory and Modern Volatility Analysis.
1. The Macro Hard Stop (The 200 EMA Logic)
Origin: Based on the institutional mandate that “Nothing good happens below the 200-day moving average.”
Function: Unlike standard super trends that flip constantly in sideways markets, this system integrates a 200-period Exponential Moving Average (EMA) as a non-negotiable "Hard Stop."
Synergy: This acts as the primary gatekeeper. Even if the volatility engine signals a "Buy," the system suppresses the signal if the price is below the Macro Baseline, effectively filtering out counter-trend traps.
2. The Volatility Engine (Squeeze Theory)
Origin: Derived from John Carter’s TTM Squeeze concept.
Function: The script identifies periods where Bollinger Bands (Standard Deviation) contract inside Keltner Channels (ATR). This indicates a period of potential energy build-up.
Synergy: The system only triggers an entry when this energy is released (Breakout) AND coincides with Linear Regression Momentum, ensuring the breakout is genuine.
3. Anti-Chop Filter (ADX Integration)
Origin: J. Welles Wilder’s Directional Movement Theory.
Function: A common failure point for trend systems is low-volatility chop. This script utilizes the Average Directional Index (ADX).
Synergy: If the ADX is below the threshold (Default: 20), the market is deemed "Choppy." The script visually represents this by painting candles GRAY, signaling a "No-Trade Zone" regardless of price action.
4. The "Run Trend" Stop Loss (Factor 4.0 ATR)
Origin: Adapted from the Turtle Trading rules regarding volatility-based stops.
Function: Standard Trailing Stops (usually Factor 3.0) are too tight for crypto or volatile equities on daily timeframes.
Optimization: This system employs a wider ATR Multiplier of 4.0. This allows the asset to fluctuate naturally within a trend without triggering a premature exit, maximizing the "Run Trend" potential.
🛠 How It Works (The Algorithm)
The script processes data in a specific order to generate a signal:
Check Macro Trend: Is Price > EMA 200? (If No, Longs are disabled).
Check Volatility: Is ADX > 20? (If No, all signals are disabled).
Check Volume: Is Current Volume > 1.2x Average Volume? (Confirmation of institutional participation).
Trigger: Has a Volatility Breakout occurred in the direction of the Macro Trend?
Execution: If ALL above are true -> Generate Signal.
🎯 Strategy Guide
1. Long Setup (Bullish)
Signal: Look for the Green "APEX LONG" Label.
Condition: The price must be ABOVE the White Line (EMA 200).
Execution: Enter at the close of the signal candle.
Stop Loss: Initial stop at the Green Trailing Line.
2. Short Setup (Bearish)
Signal: Look for the Red "APEX SHORT" Label.
Condition: The price must be BELOW the White Line (EMA 200).
Execution: Enter at the close of the signal candle.
Stop Loss: Initial stop at the Red Trailing Line.
3. Exit Rules (Crucial)
This system employs a Dual-Exit Mechanism:
Soft Exit (Profit Taking): Close the position if the price crosses the Trailing Stop Line (Green/Red line). This locks in profits during a trend reversal.
Hard Exit (Emergency): Close the position IMMEDIATELY if the price crosses the White EMA 200 Line against your trade. This prevents holding a position during a major market regime change.
⚙️ Settings
Momentum Engine: Adjust Bollinger Band/Keltner Channel lengths to tune breakout sensitivity.
Apex Filters: Toggle the EMA 200 or ADX filters on/off to adapt to different asset classes.
Risk Management: The ATR Multiplier (Default 4.0) controls the width of the trailing stop. Lower values = Tighter stops (Scalping); Higher values = Looser stops (Swing).
Disclaimer: This script is designed for trend-following on higher timeframes (4H, 1D, 1W). Please backtest on your specific asset before live trading.
SHOPPA trendBuy and Sell indicator based on golden cross and death cross. exit signals for LX (long exit) and SX (short exit)
Multi EMA (up to 6) - JamilThis indicator plots six customizable Exponential Moving Averages (EMA 1 to EMA 6) designed to help traders quickly identify market direction, trend strength, and dynamic support/resistance levels.
🔹 Key Features
Plots six EMAs simultaneously for multi-timeframe trend clarity
Helps detect trend reversals, pullbacks, and continuation setups
Ideal for scalping, intraday, swing trading, and funded challenges
Works on all markets (Gold, Forex, Crypto, Indices)
Customizable lengths and colors
Clean and lightweight — doesn’t affect chart performance
🔹 How to Use
When all EMAs are aligned and fanning out → Strong Trend
EMA compression → Low volatility / possible breakout setup
Price above all EMAs → Bullish zone
Price below all EMAs → Bearish zone
Perfect for traders who want a simple yet powerful trend-reading tool.
