Big 9 Volume - Volume indicator from exchanges with real volumeHere is a very basic indicator combining the volumes of the 9 biggest exchanges trading BTC/USD or BTC/USDT. These 9 exchanges were chosen based on the report by Bitwise Invest stating that 95% of the volume on CoinMarketCap is fake. On these 9 exchanges, however, volume data appears to be reliable. Please note BitFlyer was not included because it does not trade in USD. Please note also that data on all 9 exchanges is only available from June 2018.
Anyone is welcome to modify this and make it more elegant, this was just a quick implementation.
ابحث في النصوص البرمجية عن "btc期权交割时间"
Major Mayer MultipleAdjusted version of the BTC Mayer Multiple developed by Trace Mayer www.theinvestorspodcast.com
This version includes two novelties. The first one replaces BTC with Total Market Cap from 2016/2017 (depending on your moving averages) to present and the second is that we consider two Moving Averages to produce more detailed lows.
Volume RSI altsSo this allow you to put major alts against each other and compare the Volume RSI to each other and to the alts that you are currently looking In this example we see TRX breaking from the major pack of the other alts in 1D chart making the breakthrough up
Each alt has it own color
if you want to add more alts just copi paste and add the code for your alt to make this system better for you
here you see how XRP break from the pack show in arrow down
here on 4 h chart we see LTC is breaking before BTC (its a btc chart and LTC in orange)
makemoney-hybridSo this model is little different from moneymaker model in the following :
The buy system based on super trend , the sell system =S is based on the volume model of money maker
in the example we set 7% take profit for both long or short . you can set it even higher since btc very volatile now
in cases where it did not reach the target its made min of 3% each direction
So the buy in this system will be in true uptrend . since now the btc is falling more then going up we can make more money on shorts and wait for the longs when they come :)
the bullish and bear zone based on super system
you need to set correctly your take profit in order to make it to work . the more volatile will be the coin the better will be the results (this is the theory )
HEAVI - HawkEye Aggregated Volume IndicatorThis is combined Aggregated BTC Exchange Volume by Neobutane with HawkEye volume clone indicator by LazyBear.
Indicator includes aggregated raw BTC volume from 9 user selectable fiat and tether exchanges + Exponential MA + hawkeye bar coloring where: green is bullish volume, red - bearish and white - volume neutral to the market:
Bitfinex
Coinbase
Bitstamp
Kraken
Binance
Poloniex
Bittrex
bitFlyer
Bithumb
RSI / Stoch / SRSI / MFI / Aroon Overlay [SigmaDraconis]Combines 4 popular indicators (RSI, Stoch, SRSI, MFI) and 1 peculiar one (Aroon) in 1 for those who want to save indicators but not only.
This is an evolution of my (simpler) "RSI / Stoch / Stoch RSI (SRSI) Overlay " that you can find on my scripts.
Added bands for oversold/overbought areas (70/30 common for RSI and 80/20 for SRSI and MFI), as well as a middle 50 horizontal line.
Neutral bands around 55-45 added as well that can be hidden for less clutter. I also recommend a more transparent coloring for these since Pine script doesn't allow default transparency for horizontal lines.
By default only RSI and Stoch are activated, you can activate Aroon, MFI and SRSI on the inputs window.
Some extra notes:
* RSI, Stoch and MFI can help to strengthen one's decision as well as Aroon to predict a possible trend reversal, SRSI can show when RSI has high probability of being topped or bottomed when oversold/overbought but don't forget to look at volume and how the trend progresses that can keep SRSI above 80 or below 20 while RSI and price continues to trend, divergences are most helpful here to find possible reversal areas.
* This chart depicts some interesting divergences, as well as Stoch tops and bottoms and confluences between RSI/MFI and Stoch on some over-extended tops and bottoms that shown being good reversal zones.
RSI resistances are shown as well, failing to break above 60 or the neutral zone (this is a bearish BTC trend chart after all) or failing to gain support to break up certain levels (RSI notes a more bullish trend when consistently above 60 and more bearish below 40).
If you like it and use it to profit, please tip me below :)
Tip jars:
BTC: 15nMBiEGVrdGcu9C1h6QRcTNRvugHkqrMQ
ETH: 0xC33845946c48B61fBCbEA0367ec2238CaF2b73bc
BTS: sigma-draconis
U&Dif price has moved up since 1 to 3 candles ago = buy
if price has moved down since 1 to 3 candles ago = sell
has internal SL & TP
tested on
BITFINEX:ETHUSD
BITFINEX:BTCUSD
BITFINEX:LTCUSD
BITFINEX:ETHBTC
4 hour charts
XRPBTC long : BTCUSD shortIt will be an index using the price delivered by Bitfinex exchanges. It is a very simple indicator, but it is a recommended index for those who want to see XRP while keeping the risk of price fluctuation of BTC down. The code is simple and you can use XRP in the same way by changing it to another alto. There is a big gap in the prices of BTC and XRP, so we adjust the values so that the indicators are easy to see.
Relative Estimated Price REP by KIVANÇ fr3762Relative Estimated Price (REP) Indicator shows the estimated price calculated if the tickerid made the same value changes (in %) during a certain period.
The default value of the lookback period is 50.
In the given XRPUSD chart you can see that XRPUSD has a value of 0.26480 and the RPC indicator shows the value of 0.38099.
This means that XRP would be 0.38099USD if it was fully made the same percentage moves with BTC , we can say that XRP is RELATIVELY cheap according to BTC price moves.
Conversely XRP would be RELATIVELY expensive if the last value of REP was lower then current XRP price.
users can choose the relative base price in calculation of REP between 1-5 which are:
1=BTCUSD, 2=ETHUSD, 3=EURTRY(Euro/Turkish Lira), 4=USDTRY (Dollar/Turkish Lira), 5=BIST100 (Istanbul Stock Exchange)
I personally advise you to use this indicator for daily charts in Tradingview to have more accurate estimated prices because of the website's calculation.
Developed by KIVANÇ
[NG] Indicator - Altcoin Alpha - v1(Created for Client)
Alpha (Unique price action of asset) indicator for ALTcoins implementation, taking `BINANCE:BTCUSDT` as the market reference. Can be improved by adding more BTC charts from more sources, so as to get a unified chart of BTC for market representation.
Set `alpha period` to a value, wherein you want to see the unique price action of the asset. For short term trend, a value of 24 is good for `1H` charts (1 day), and value of 168 is good for long term trends on `1H` charts (1 week trend).
Corresponding values of `beta period` should be `168` (1 week for 1 day alpha) and `720` (1 month for 1 week alpha period).
You can set `alpha` and `beta` period as per your requirements.
Regards,
TSP Volume Change Big Small// Better Display of Volume change
// green candle : Big volume change
// red Candle : Small volume change
// Default for BTC m5
// Big volume are limited up to $limup% 5%
// Pump : Volume over $limgreen% 2%
// Flat : Very Small Volume under $limdo% 0.2%
// Adjust based on volatility / TF
// BTC/USD 1h : 4,2,0.25
Quote asset VolumeVolume expressed in quote asset units. For pair DOGE/BTC the volume is shown in BTC, instead of DOGE.
Values are imprecise, because each candle's price is calculated as (O+H+L+C)/4, instead of a weighted average one, which I couldn't obtain.
Noro's Trend MAs Strategy v1.8Trade strategy which uses only 2 MA.
The slow MA (blue) is used for definition of a trend
The fast MA (red) is used for an entrance to the transaction
For:
- For H1
- For crypto/fiat or crypto/crypto
- Good for "BTC/USD", "ETH/USD", "ETH/BTC"
Recomended:
Long = true (if it is profitable as a result of backtests)
Short = true (if it is profitable as a result of backtests)
Stops = false
Stop, % = any
OHLC4 = any
Use Fast MA = true
Fast MA Period = 5
Slow MA Period = 21
Bars Q = (2 for "bitcoin/fiat" or 1 for "crypto/fiat" or 0 for "crypto/crypto")
In the new version 1.8
- The second PriceChannel is added
- Profit became more
- Losses became less
- The unnecessary types of MA are removed
Bitcoin momentum correlation This is a pretty simple indicator, it measures the momentum of bitcoin as compared to usd,eur,eth,dash, and ltc, which you can see in all of the blue lines. If the red line is above zero then it means the overall value of btc is going up, opposite for down. The Ema_window controls how smooth the signal is. If you shorten the Ema_window parameter and open this on higher timeframe btc charts then the zero crossing gives pretty solid signals, despite being pretty choppy. A good way to interpret this is that if all the blue lines are moving in the same direction at once without disagreement, then the value of bitcoin has good momentum.
Mildly more technically:
Momentum is measured in the first derivative of an EMA for each ticker. To normalize the different values against each other they are all divided by their local maximums, which can be chosen in the parameter window, but shouldn't make a huge difference. All the checked values are then summed, as shown in the red line. To include a value into the red line simply keep it checked. Take a look at the script, it's kind of easy on the eyes.
It's pretty handy to look at, but doesn't seem too worthwhile to pursue much further. If someone wants much more out of the script then feel free to message me.
Remember rules #1 & #2
Don't lose money.
Happy trading
RSI+BSIThis script simply plots the current instruments RSI as well as Bitcoin's RSI from bitfinex. Helpful to identify when an alt is performing stronger than BTC or if BTC is dragging the alt down.
Volume Conversion IndicatorVolume Conversion Indicator
The volume conversion indicator is much like the in-built volume indicator. This particular volume indicator allows you to find out how much of something has been traded in a given timeframe.
This is done by multiplying volume by the average price at that point.
What does this mean?
Well, say, for example, you were watching DGB/BTC (DigiByte/Bitcoin). Instead of the volume being displayed in the amount of DGB traded, the amount of BTC traded is displayed instead.
Feel free to comment... Hope this helps :D
Indicator: Schaff Trend Cycle (STC)Another new indicator for TV community :)
STC detects up and down trends long before the MACD. It does this by using the same exponential moving averages (EMAs), but adds a cycle component to factor instrument cycle trends. STC gives more accuracy and reliability than the MACD.
More info: www.investopedia.com
Feel free to "Make mine" this chart and use the indicator in your charts. Appreciate any feedback on how effective this is for your instrument (I have tested this only with BTC).
For people trading BTC:
-------------------------------
Try 3/10 or 9/30 for MACD (fastLength/slowLength). They seem to catch the cycles better than the defaults. :)
🎯 Wyckoff Order Block Entry System🎯 Wyckoff Order Block Entry System
📝 INDICATOR DESCRIPTION
🎯 Wyckoff Order Block Entry System Short Description:
Professional institutional zone trading combined with Wyckoff methodology. Identifies high-probability entries where smart money meets classic price action patterns.
Full Description:
Wyckoff Order Block Entry System is a precision trading tool that combines two powerful concepts:
Order Blocks - Institutional zones where large players place their orders
Wyckoff Method - Classic price action patterns revealing smart money behavior
🎯 What Makes This Different?
Unlike traditional indicators that flood your chart with signals, this system only triggers entries when BOTH conditions are met:
Price enters an institutional Order Block zone (current timeframe OR higher timeframe)
A Wyckoff pattern occurs (Spring, SOS, Upthrust, or SOW)
This dual-confirmation approach ensures you're trading with institutional flow at optimal entry points.
📊 Key Features:
✅ Order Block Detection
Automatically identifies institutional buying/selling zones
Current timeframe order blocks (solid lines)
Higher timeframe order blocks (dashed lines) for stronger zones
Customizable strength and extension settings
✅ 4 Wyckoff Entry Patterns
SPRING (Bullish Reversal): Fake breakdown below support → Quick recovery
SOS (Sign of Strength): Strong bullish candle after accumulation
UPTHRUST (Bearish Reversal): Fake breakout above resistance → Quick rejection
SOW (Sign of Weakness): Strong bearish candle after distribution
✅ Clean Visual Design
Minimalist approach - only essential information
Color-coded zones (Green = Bullish, Red = Bearish, Cyan/Magenta = HTF)
Clear entry signals with pattern type labels
No chart clutter - focus on what matters
✅ Multi-Timeframe Analysis
Integrates higher timeframe order blocks
HTF signals marked with "+HTF" tag for extra confidence
Fully customizable HTF selection (H1, H4, Daily, etc.)
✅ Smart Alerts
Entry signal alerts (Long/Short)
Order block formation alerts
HTF order block alerts
Customizable alert messages
💡 How To Use:
Setup: Add indicator to your chart, configure HTF timeframe (default H1)
Wait: Let order blocks form (green/red boxes appear)
Watch: Price returns to order block zone
Entry: Signal appears when Wyckoff pattern confirms
Trade: Enter with the signal, stop below/above order block
📈 Best For:
Forex pairs (all majors and crosses)
Gold (XAUUSD)
Crypto (BTC, ETH, etc.)
Indices (SPX, NAS100, etc.)
Stocks
Commodities
⏱️ Recommended Timeframes:
M15 for scalping
M30 for day trading
H1 for swing trading
H4 for position trading
🎯 Win Rate Expectations:
Current TF signals: 60-70%
HTF signals (+HTF tag): 70-80%
Spring/Upthrust patterns: Highest probability
Works on ALL liquid markets
⚙️ Customizable Settings:
Order block detection parameters
HTF timeframe selection
Wyckoff sensitivity (swing length, volume threshold)
Zone extension duration
Color schemes
📚 Trading Strategy:
This indicator works best when:
Trading in the direction of higher timeframe trend
Using proper risk management (1-2% per trade)
Placing stops just outside order block zones
Taking profits at opposite order blocks
Focusing on HTF signals for higher quality
🔒 Risk Management:
Always use stop losses! Recommended placement:
LONG: 10-20 pips below order block
SHORT: 10-20 pips above order block
Target: Minimum 1:2 risk/reward ratio
💎 Why Traders Love This System:
"Finally, an indicator that doesn't spam my chart with useless signals!" - The quality-over-quantity approach means you only get high-probability setups.
"The HTF order blocks changed my trading!" - Multi-timeframe analysis built-in removes the need for manual higher timeframe checks.
"Wyckoff + Order Blocks = Perfect combination!" - Two proven concepts working together create powerful confluence.
📊 Universal Application:
This system works on ANY liquid market with sufficient volume:
✅ Forex (EUR/USD, GBP/USD, USD/JPY, etc.)
✅ Commodities (Gold, Silver, Oil, etc.)
✅ Indices (S&P 500, NASDAQ, DAX, etc.)
✅ Cryptocurrencies (Bitcoin, Ethereum, etc.)
✅ Stocks (Large cap with good liquidity)
🎓 Educational Value:
Beyond just signals, this indicator teaches you:
How institutional traders think
Where smart money places orders
Classic Wyckoff accumulation/distribution patterns
Multi-timeframe analysis techniques
⚡ Performance:
Lightning-fast calculations
No repainting
Real-time signal generation
Clean code, optimized for speed
🚀 Get Started:
Add to your favorite chart
Adjust HTF timeframe to match your trading style
Wait for high-quality signals
Trade with confidence
Remember: Quality beats quantity. This system prioritizes precision over frequency. You might see 2-5 signals per day on M30 - and that's exactly the point. Each signal is carefully filtered for maximum probability.
Ready to trade like institutions?
👉 Add this indicator to your chart now
👉 Configure your preferred HTF timeframe
👉 Start catching high-probability setups
👉 Trade smarter, not harder
Questions or feedback? Drop a comment below!
Found this useful? Hit that ⭐ button and share with fellow traders!
Happy Trading! 🚀📈
VWAP + Volume Spikes See Where Smart Money ExhaustsVolume tells the truth. VWAP tells the bias. This script shows both — live.
If you trade intraday momentum, reversals, or liquidity sweeps, this indicator is built for you.
It shows where volume spikes hit extreme levels, anchored around VWAP and its dynamic bands, so you can instantly spot capitulation or hidden absorption.
🎯 What This Indicator Does
✅ Plots VWAP — session-anchored, updates automatically
✅ Adds dynamic VWAP bands — standard deviation envelopes showing volatility context
✅ Highlights volume spikes — colored candles + background for abnormal prints
✅ Includes alerts — “Volume Spike”, “VWAP Cross”, or a combined alert with direction
✅ Clean visual design — instantly readable in fast markets
It’s your visual orderflow radar — whether you’re trading gold, indices, or small caps.
🔍 Why It Works
Institutions build and unwind positions around VWAP.
Retail often chases volume… this script shows you when that volume becomes too extreme.
A spike above VWAP near resistance? → Likely distribution.
A spike below VWAP near support? → Likely capitulation.
Combine volume exhaustion + VWAP context, and you’ll see market turning points form before most indicators react.
⚙️ Inputs You Can Tune
Bands lookback: adjusts how reactive the VWAP bands are
Band width (σ): set how tight or wide your deviation envelope is
Volume baseline length: controls how “abnormal” a spike must be
Spike threshold: multiplier vs. average volume
Toggle color-coding, bands, and labels
Default settings work well across 1m–15m intraday charts and 1h–4h swing frames.
💡 How Traders Use It
1️⃣ Fade Parabolics:
When a green spike candle pierces upper VWAP band on high volume → smart money unloading.
Look for rejection and short into VWAP.
2️⃣ Catch Capitulations:
When a red spike candle dumps below lower VWAP band → panic selling.
Watch for stabilization and long back to VWAP.
3️⃣ VWAP Rotation Plays:
Alerts for price crossing VWAP help you spot shift in intraday control.
Above VWAP = buyers in charge.
Below VWAP = sellers in charge.
🧠 Best Practices
Pair it with Volume Profile or Delta/Flow tools to confirm exhaustion.
Don’t chase — wait for spike confirmation + reversal candle.
Use it on liquid tickers (NASDAQ, SPY, GOLD, BTC, etc.).
Great for Dux-style small-cap shorts or index pullbacks.
🔔 Alerts Ready
Choose from:
Volume Spike (single-bar explosion)
VWAP Cross Up/Down (trend shift confirmation)
One Combined Alert (any signal, includes ticker, price, and volume)
Set once — get real-time push notifications, Telegram, or webhook signals.
📊 My Favorite Setups
US100 / NASDAQ: fade rallies above VWAP + spike
Gold / Silver: trade reversals from VWAP bands
Small caps: short back-side after volume climax
ES, DAX, Oil: scalp VWAP rotation with confluence
❤️ Support This Work
I release free and premium scripts weekly — combining smart money concepts, VWAP tools, and volume analytics.
👉 Follow me on TradingView for more indicators and setups.
👉 Comment “🔥” if you want me to post the multi-timeframe VWAP + Volume Pressure version next.
👉 Share this with your team — it helps the community grow.
Range Oscillator Strategy + Stoch Confirm🔹 Short summary
This is a free, educational long-only strategy built on top of the public “Range Oscillator” by Zeiierman (used under CC BY-NC-SA 4.0), combined with a Stochastic timing filter, an EMA-based exit filter and an optional risk-management layer (SL/TP and R-multiple exits). It is NOT financial advice and it is NOT a magic money machine. It’s a structured framework to study how range-expansion + momentum + trend slope can be combined into one rule-based system, often with intentionally RARE trades.
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0. Legal / risk disclaimer
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• This script is FREE and public. I do not charge any fee for it.
• It is for EDUCATIONAL PURPOSES ONLY.
• It is NOT financial advice and does NOT guarantee profits.
• Backtest results can be very different from live results.
• Markets change over time; past performance is NOT indicative of future performance.
• You are fully responsible for your own trades and risk.
Please DO NOT use this script with money you cannot afford to lose. Always start in a demo / paper trading environment and make sure you understand what the logic does before you risk any capital.
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1. About default settings and risk (very important)
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The script is configured with the following defaults in the `strategy()` declaration:
• `initial_capital = 10000`
→ This is only an EXAMPLE account size.
• `default_qty_type = strategy.percent_of_equity`
• `default_qty_value = 100`
→ This means 100% of equity per trade in the default properties.
→ This is AGGRESSIVE and should be treated as a STRESS TEST of the logic, not as a realistic way to trade.
TradingView’s House Rules recommend risking only a small part of equity per trade (often 1–2%, max 5–10% in most cases). To align with these recommendations and to get more realistic backtest results, I STRONGLY RECOMMEND you to:
1. Open **Strategy Settings → Properties**.
2. Set:
• Order size: **Percent of equity**
• Order size (percent): e.g. **1–2%** per trade
3. Make sure **commission** and **slippage** match your own broker conditions.
• By default this script uses `commission_value = 0.1` (0.1%) and `slippage = 3`, which are reasonable example values for many crypto markets.
If you choose to run the strategy with 100% of equity per trade, please treat it ONLY as a stress-test of the logic. It is NOT a sustainable risk model for live trading.
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2. What this strategy tries to do (conceptual overview)
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This is a LONG-ONLY strategy designed to explore the combination of:
1. **Range Oscillator (Zeiierman-based)**
- Measures how far price has moved away from an adaptive mean.
- Uses an ATR-based range to normalize deviation.
- High positive oscillator values indicate strong price expansion away from the mean in a bullish direction.
2. **Stochastic as a timing filter**
- A classic Stochastic (%K and %D) is used.
- The logic requires %K to be below a user-defined level and then crossing above %D.
- This is intended to catch moments when momentum turns up again, rather than chasing every extreme.
3. **EMA Exit Filter (trend slope)**
- An EMA with configurable length (default 70) is calculated.
- The slope of the EMA is monitored: when the slope turns negative while in a long position, and the filter is enabled, it triggers an exit condition.
- This acts as a trend-protection exit: if the medium-term trend starts to weaken, the strategy exits even if the oscillator has not yet fully reverted.
4. **Optional risk-management layer**
- Percentage-based Stop Loss and Take Profit (SL/TP).
- Risk/Reward (R-multiple) exit based on the distance from entry to SL.
- Implemented as OCO orders that work *on top* of the logical exits.
The goal is not to create a “holy grail” system but to serve as a transparent, configurable framework for studying how these concepts behave together on different markets and timeframes.
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3. Components and how they work together
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(1) Range Oscillator (based on “Range Oscillator (Zeiierman)”)
• The script computes a weighted mean price and then measures how far price deviates from that mean.
• Deviation is normalized by an ATR-based range and expressed as an oscillator.
• When the oscillator is above the **entry threshold** (default 100), it signals a strong move away from the mean in the bullish direction.
• When it later drops below the **exit threshold** (default 30), it can trigger an exit (if enabled).
(2) Stochastic confirmation
• Classic Stochastic (%K and %D) is calculated.
• An entry requires:
- %K to be below a user-defined “Cross Level”, and
- then %K to cross above %D.
• This is a momentum confirmation: the strategy tries to enter when momentum turns up from a pullback rather than at any random point.
(3) EMA Exit Filter
• The EMA length is configurable via `emaLength` (default 70).
• The script monitors the EMA slope: it computes the relative change between the current EMA and the previous EMA.
• If the slope turns negative while the strategy holds a long position and the filter is enabled, it triggers an exit condition.
• This is meant to help protect profits or cut losses when the medium-term trend starts to roll over, even if the oscillator conditions are not (yet) signalling exit.
(4) Risk management (optional)
• Stop Loss (SL) and Take Profit (TP):
- Defined as percentages relative to average entry price.
- Both are disabled by default, but you can enable them in the Inputs.
• Risk/Reward Exit:
- Uses the distance from entry to SL to project a profit target at a configurable R-multiple.
- Also optional and disabled by default.
These exits are implemented as `strategy.exit()` OCO orders and can close trades independently of oscillator/EMA conditions if hit first.
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4. Entry & Exit logic (high level)
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A) Time filter
• You can choose a **Start Year** in the Inputs.
• Only candles between the selected start date and 31 Dec 2069 are used for backtesting (`timeCondition`).
• This prevents accidental use of tiny cherry-picked windows and makes tests more honest.
B) Entry condition (long-only)
A long entry is allowed when ALL the following are true:
1. `timeCondition` is true (inside the backtest window).
2. If `useOscEntry` is true:
- Range Oscillator value must be above `entryLevel`.
3. If `useStochEntry` is true:
- Stochastic condition (`stochCondition`) must be true:
- %K < `crossLevel`, then %K crosses above %D.
If these filters agree, the strategy calls `strategy.entry("Long", strategy.long)`.
C) Exit condition (logical exits)
A position can be closed when:
1. `timeCondition` is true AND a long position is open, AND
2. At least one of the following is true:
- If `useOscExit` is true: Oscillator is below `exitLevel`.
- If `useMagicExit` (EMA Exit Filter) is true: EMA slope is negative (`isDown = true`).
In that case, `strategy.close("Long")` is called.
D) Risk-management exits
While a position is open:
• If SL or TP is enabled:
- `strategy.exit("Long Risk", ...)` places an OCO stop/limit order based on the SL/TP percentages.
• If Risk/Reward exit is enabled:
- `strategy.exit("RR Exit", ...)` places an OCO order using a projected R-multiple (`rrMult`) of the SL distance.
These risk-based exits can trigger before the logical oscillator/EMA exits if price hits those levels.
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5. Recommended backtest configuration (to avoid misleading results)
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To align with TradingView House Rules and avoid misleading backtests:
1. **Initial capital**
- 10 000 (or any value you personally want to work with).
2. **Order size**
- Type: **Percent of equity**
- Size: **1–2%** per trade is a reasonable starting point.
- Avoid risking more than 5–10% per trade if you want results that could be sustainable in practice.
3. **Commission & slippage**
- Commission: around 0.1% if that matches your broker.
- Slippage: a few ticks (e.g. 3) to account for real fills.
4. **Timeframe & markets**
- Volatile symbols (e.g. crypto like BTCUSDT, or major indices).
- Timeframes: 1H / 4H / **1D (Daily)** are typical starting points.
- I strongly recommend trying the strategy on **different timeframes**, for example 1D, to see how the behaviour changes between intraday and higher timeframes.
5. **No “caution warning”**
- Make sure your chosen symbol + timeframe + settings do not trigger TradingView’s caution messages.
- If you see warnings (e.g. “too few trades”), adjust timeframe/symbol or the backtest period.
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5a. About low trade count and rare signals
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This strategy is intentionally designed to trade RARELY:
• It is **long-only**.
• It uses strict filters (Range Oscillator threshold + Stochastic confirmation + optional EMA Exit Filter).
• On higher timeframes (especially **1D / Daily**) this can result in a **low total number of trades**, sometimes WELL BELOW 100 trades over the whole backtest.
TradingView’s House Rules mention 100+ trades as a guideline for more robust statistics. In this specific case:
• The **low trade count is a conscious design choice**, not an attempt to cherry-pick a tiny, ultra-profitable window.
• The goal is to study a **small number of high-conviction long entries** on higher timeframes, not to generate frequent intraday signals.
• Because of the low trade count, results should NOT be interpreted as statistically strong or “proven” – they are only one sample of how this logic would have behaved on past data.
Please keep this in mind when you look at the equity curve and performance metrics. A beautiful curve with only a handful of trades is still just a small sample.
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6. How to use this strategy (step-by-step)
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1. Add the script to your chart.
2. Open the **Inputs** tab:
- Set the backtest start year.
- Decide whether to use Oscillator-based entry/exit, Stochastic confirmation, and EMA Exit Filter.
- Optionally enable SL, TP, and Risk/Reward exits.
3. Open the **Properties** tab:
- Set a realistic account size if you want.
- Set order size to a realistic % of equity (e.g. 1–2%).
- Confirm that commission and slippage are realistic for your broker.
4. Run the backtest:
- Look at Net Profit, Max Drawdown, number of trades, and equity curve.
- Remember that a low trade count means the statistics are not very strong.
5. Experiment:
- Tweak thresholds (`entryLevel`, `exitLevel`), Stochastic settings, EMA length, and risk params.
- See how the metrics and trade frequency change.
6. Forward-test:
- Before using any idea in live trading, forward-test on a demo account and observe behaviour in real time.
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7. Originality and usefulness (why this is more than a mashup)
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This script is not intended to be a random visual mashup of indicators. It is designed as a coherent, testable strategy with clear roles for each component:
• Range Oscillator:
- Handles mean vs. range-expansion states via an adaptive, ATR-normalized metric.
• Stochastic:
- Acts as a timing filter to avoid entering purely on extremes and instead waits for momentum to turn.
• EMA Exit Filter:
- Trend-slope-based safety net to exit when the medium-term direction changes against the position.
• Risk module:
- Provides practical, rule-based exits: SL, TP, and R-multiple exit, which are useful for structuring risk even if you modify the core logic.
It aims to give traders a ready-made **framework to study and modify**, not a black box or “signals” product.
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8. Limitations and good practices
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• No single strategy works on all markets or in all regimes.
• This script is long-only; it does not short the market.
• Performance can degrade when market structure changes.
• Overfitting (curve fitting) is a real risk if you endlessly tweak parameters to maximise historical profit.
Good practices:
- Test on multiple symbols and timeframes.
- Focus on stability and drawdown, not only on how high the profit line goes.
- View this as a learning tool and a basis for your own research.
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9. Licensing and credits
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• Core oscillator idea & base code:
- “Range Oscillator (Zeiierman)”
- © Zeiierman, licensed under CC BY-NC-SA 4.0.
• Strategy logic, Stochastic confirmation, EMA Exit Filter, and risk-management layer:
- Modifications by jokiniemi.
Please respect both the original license and TradingView House Rules if you fork or republish any part of this script.
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10. No payments / no vendor pitch
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• This script is completely FREE to use on TradingView.
• There is no paid subscription, no external payment link, and no private signals group attached to it.
• If you have questions, please use TradingView’s comment system or private messages instead of expecting financial advice.
Use this script as a tool to learn, experiment, and build your own understanding of markets.
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11. Example backtest settings used in screenshots
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To avoid any confusion about how the results shown in screenshots were produced, here is one concrete example configuration:
• Symbol: BTCUSDT (or similar major BTC pair)
• Timeframe: 1D (Daily)
• Backtest period: from 2018 to the most recent data
• Initial capital: 10 000
• Order size type: Percent of equity
• Order size: 2% per trade
• Commission: 0.1%
• Slippage: 3 ticks
• Risk settings: Stop Loss and Take Profit disabled by default, Risk/Reward exit disabled by default
• Filters: Range Oscillator entry/exit enabled, Stochastic confirmation enabled, EMA Exit Filter enabled
If you change any of these settings (symbol, timeframe, risk per trade, commission, slippage, filters, etc.), your results will look different. Please always adapt the configuration to your own risk tolerance, market, and trading style.
Auto Fibonacci RetraceNOTE: This script is for educational purposes only.
This Pine Script v6 indicator automates the drawing of Fibonacci retracement levels on a TradingView chart based on detected pivot highs and lows. It's designed to identify the most recent swing points in a price trend and plot horizontal lines at standard Fibonacci ratios (0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, 100%), along with optional labels for each level. The script is useful for traders who want dynamic, hands-free Fib retracements that update as new pivots form, helping to spot potential support/resistance zones without manual intervention.
Key Features
Automatic Pivot Detection: Uses TradingView's built-in ta.pivothigh and ta.pivotlow functions to find recent swing highs and lows. The sensitivity is adjustable via user inputs for "Left Bars" and "Right Bars" (default: 5 each), which define how many bars are checked on either side to confirm a pivot.
Trend Direction Awareness: Determines if the current swing is an uptrend (recent high after low) or downtrend (recent low after high) and orients the Fib levels accordingly—starting from the low in uptrends or high in downtrends.
Dynamic Drawing:
Plots dashed horizontal lines extending to the right of the chart for each Fib level.
Colors are predefined for visual distinction (e.g., blue for 23.6%, orange for 61.8%).
Lines and labels are cleared and redrawn only when a new pivot is detected or on initial load to prevent chart clutter.
Customizable Labels: Optional labels show the percentage (e.g., "61.8%") and can be positioned on the "Left" (at the swing start) or "Right" (pinned to the current bar, updating dynamically). Labels use semi-transparent backgrounds for readability.
Performance Optimizations: Uses arrays to manage lines and labels efficiently, with reverse-indexed loops for safe deletion. The max_bars_back=500 ensures it handles historical data without excessive computation.
User Inputs:
Left/Right Bars: Tune pivot detection (higher values for major trends, lower for shorter swings).
Show Fib Levels/Labels: Toggle visibility.
Label Position: "Left" or "Right" for placement flexibility.
Usage Instructions
Adding to Chart: Copy-paste into TradingView's Pine Editor, save as a new indicator, and add it to your chart via the "Indicators" menu.
Customization: Adjust inputs in the indicator settings panel. For example, set Left/Right Bars to 10 for daily charts in strong trends.
Best Practices:
Use on trending markets (e.g., stocks, forex, crypto like BTC/USD); avoid choppy sideways action.
Combine with other indicators (e.g., RSI for overbought/oversold confirmation) for better trade signals.
Test on historical data—zoom out to see how it redraws on past swings.
Limitations: Relies on pivot functions, so it may lag slightly (pivots confirm after "Right Bars"). Not a trading strategy—use for analysis only. No alerts built-in, but you can add alertcondition if extending it.
Potential Enhancements: Add extensions (e.g., 161.8%), user-defined levels, or alerts on price touches via simple modifications.
This script provides a clean, efficient way to visualize Fib retracements automatically, saving time compared to manual drawing. If you need further tweaks or integration into a full strategy, let me know!
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite
This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion .
Core Architecture
Why These Components Are Combined:
The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention.
The Four Trading Agents
1. Spoofing Detector Agent 🎭
Detects iceberg orders through persistent volume at similar price levels over 5 bars
Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8× average)
Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model)
Signal Logic: Contrarian—fades false breakouts caused by institutional manipulation
Best Markets: Consolidations, institutional trading windows, low-liquidity hours
2. Exhaustion Detector Agent ⚡
Calculates RSI divergence between price movement and momentum indicator over 5-bar window
Detects VWAP exhaustion (price at 2σ bands with declining volume)
Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure
Signal Logic: Counter-trend—enters when momentum extreme shows weakness
Best Markets: Trending markets reaching climax points, over-extended moves
3. Liquidity Void Detector Agent 💧
Measures Bollinger Band squeeze (width <60% of 50-period average)
Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike
Detects hidden liquidity absorption (volume >2× average with range <0.3× ATR)
Signal Logic: Breakout anticipation—enters after liquidity grab but before main move
Best Markets: Range-bound pre-breakout, volatility compression zones
4. Mean Reversion Agent 📊
Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ±2σ)
Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model)
Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability)
Signal Logic: Statistical reversion—enters when price deviates significantly from statistical equilibrium
Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments
Adaptive Selection: Multi-Armed Bandit System
The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance:
Thompson Sampling (Default - Recommended):
Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent)
Balances exploration (trying underused agents) vs. exploitation (using proven winners)
Each agent's win/loss history informs its selection probability
Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation
UCB1 (Upper Confidence Bound):
Calculates confidence intervals using: average_reward + sqrt(2 × ln(total_pulls) / agent_pulls)
Deterministic algorithm favoring agents with high uncertainty (potential upside)
More conservative than Thompson Sampling
Epsilon-Greedy:
Exploits best-performing agent (1-ε)% of the time
Explores randomly ε% of the time (default 10%, configurable 1-50%)
Simple, transparent, easily tuned via epsilon parameter
Gradient Bandit:
Uses softmax probability distribution over agent preference weights
Updates weights via gradient ascent based on rewards
Best for Blend mode where all agents contribute
Selection Modes:
Switch Mode: Uses only the selected agent's signal (clean, decisive)
Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified)
Lock Agent Feature:
Optional manual override to force one specific agent
Useful after identifying which agent dominates your specific instrument
Only applies in Switch mode
Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion
Memory System
Dual-Layer Architecture:
Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50)
Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars)
Memory Boost Mechanism: Recent performance modulates agent scores by up to ±20%
Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage
Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds)
How Memory Works:
Agent generates signal → outcome tracked after 8 bars (performance horizon)
Result stored in short-term memory (win = 1.0, loss = 0.0)
Short-term average influences agent's future scores (positive feedback loop)
After threshold met (default 10 results), episode averaged into long-term storage
Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost
Market Microstructure Analysis
These advanced metrics quantify institutional order flow dynamics:
Order Flow Toxicity (Simplified VPIN):
Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol)
Detects informed trading activity (institutional players with non-public information)
Values >0.4 indicate "toxic flow" (informed traders active)
Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification
Price Impact Analysis (Simplified Kyle's Lambda):
Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10)
Low values = large orders with minimal price impact ( stealth accumulation )
High values = retail-dominated moves with high slippage
Lite Approximation: Uses simplified denominator instead of regression-based signed order flow
Market Randomness (Entropy Analysis):
Counts unique price changes over 20 bars / 20
Measures market predictability
High entropy (>0.6) = human-driven, chaotic price action
Low entropy (<0.25) = algorithmic trading dominance (predictable patterns)
Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -Σ p(x)·log₂(p(x))
Order Clustering (Simplified Hawkes Process):
Tracks self-exciting event intensity (coordinated order activity)
Decays at 0.9× per bar, spikes +1.0 when volume >1.5× average
High intensity (>0.7) indicates clustering (potential spoofing/accumulation)
Lite Approximation: Simple exponential decay instead of full λ(t) = μ + Σ α·exp(-β(t-tᵢ)) with MLE
Signal Generation Process
Multi-Stage Validation:
Stage 1: Agent Scoring
Each agent calculates internal score based on its detection criteria
Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier)
Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0)
Stage 2: Memory Boost
Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance)
Successful agents get amplified, failing agents get dampened
Stage 3: Bandit Selection/Blending
If Adaptive Mode ON:
Switch: Bandit selects single best agent, uses only its signal
Blend: All agents combined using softmax-weighted confidence scores
If Adaptive Mode OFF:
Traditional consensus voting with confidence-squared weighting
Signal fires when consensus exceeds threshold (default 70%)
Stage 4: Confirmation Filter
Raw signal must repeat for consecutive bars (default 3, configurable 2-4)
Minimum confidence threshold: 0.25 (25%) enforced regardless of mode
Trend alignment check: Long signals require trend_score ≥ -2, Short signals require trend_score ≤ 2
Stage 5: Cooldown Enforcement
Minimum bars between signals (default 10, configurable 5-15)
Prevents over-trading during choppy conditions
Stage 6: Performance Tracking
After 8 bars (performance horizon), signal outcome evaluated
Win = price moved in signal direction, Loss = price moved against
Results fed back into memory and bandit statistics
Trading Modes (Presets)
Pre-configured parameter sets:
Conservative: 85% consensus, 4 confirmations, 15-bar cooldown
Expected: 60-70% win rate, 3-8 signals/week
Best for: Swing trading, capital preservation, beginners
Balanced: 70% consensus, 3 confirmations, 10-bar cooldown
Expected: 55-65% win rate, 8-15 signals/week
Best for: Day trading, most traders, general use
Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown
Expected: 50-58% win rate, 15-30 signals/week
Best for: Scalping, high-frequency trading, active management
Elite: 75% consensus, 3 confirmations, 12-bar cooldown
Expected: 58-68% win rate, 5-12 signals/week
Best for: Selective trading, high-conviction setups
Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown
Expected: Varies based on learning
Best for: Experienced users leveraging bandit system
How to Use
1. Initial Setup (5 Minutes):
Select Trading Mode matching your style (start with Balanced)
Enable Adaptive Learning (recommended for automatic agent selection)
Choose Thompson Sampling algorithm (best all-around performance)
Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume)
2. Agent Tuning (Optional):
Adjust Agent Sensitivity multipliers (0.5-2.0):
<0.8 = Highly selective (fewer signals, higher quality)
0.9-1.2 = Balanced (recommended starting point)
1.3 = Aggressive (more signals, lower individual quality)
Monitor dashboard for 20-30 signals to identify dominant agent
If one agent consistently outperforms, consider using Lock Agent feature
3. Bandit Configuration (Advanced):
Blend Temperature (0.1-2.0):
0.3 = Sharp decisions (best agent dominates)
0.5 = Balanced (default)
1.0+ = Smooth (equal weighting, democratic)
Memory Decay (0.8-0.99):
0.90 = Fast adaptation (volatile markets)
0.95 = Balanced (most instruments)
0.97+ = Long memory (stable trends)
4. Signal Interpretation:
Green triangle (▲): Long signal confirmed
Red triangle (▼): Short signal confirmed
Dashboard shows:
Active agent (highlighted row with ► marker)
Win rate per agent (green >60%, yellow 40-60%, red <40%)
Confidence bars (█████ = maximum confidence)
Memory size (short-term buffer count)
Colored zones display:
Entry level (current close)
Stop-loss (1.5× ATR)
Take-profit 1 (2.0× ATR)
Take-profit 2 (3.5× ATR)
5. Risk Management:
Never risk >1-2% per signal (use ATR-based stops)
Signals are entry triggers, not complete strategies
Combine with your own market context analysis
Consider fundamental catalysts and news events
Use "Confirming" status to prepare entries (not to enter early)
6. Memory Persistence (Optional):
After 50-100 trades, check Memory Export Panel
Record displayed alpha/beta/weight values for each agent
Record VPIN and Kyle threshold values
Enable "Restore From Memory" and input saved values to continue learning
Useful when switching timeframes or restarting indicator
Visual Components
On-Chart Elements:
Spectral Layers: EMA8 ± 0.5 ATR bands (dynamic support/resistance, colored by trend)
Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers)
Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence × ATR)
Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal)
Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets
Signal Markers: Triangle up/down at validated entry points
Dashboard (Configurable Position & Size):
Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear)
Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus)
Agent Performance Table: Real-time win%, confidence, and memory stats
Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled)
Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress
Memory Panel (Configurable Position & Size):
Live Parameter Export: Alpha, beta, and weight values per agent
Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold
Save Reminder: Visual indicator if parameters should be recorded
What Makes This Original
This script's originality lies in three key innovations:
1. Genuine Meta-Learning Framework:
Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimization—it's real-time learning.
2. Episodic Memory Architecture with Transfer Learning:
The dual-layer memory system mimics human learning patterns:
Short-term memory captures recent performance (recency bias)
Long-term memory preserves historical patterns (experience)
Automatic transfer mechanism consolidates knowledge
Memory boost creates positive feedback loops (successful strategies become stronger)
This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates.
3. Institutional Microstructure Integration:
Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis.
Mashup Justification:
The four agents are combined specifically for risk diversification across failure modes:
Spoofing Detector: Prevents false breakout losses from manipulation
Exhaustion Detector: Prevents chasing extended trends into reversals
Liquidity Void: Exploits volatility compression (different regime than trending)
Mean Reversion: Provides mathematical anchoring when patterns fail
The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals.
Why "ML-lite"? Simplifications and Approximations
This is the "lite" version due to necessary simplifications for Pine Script execution:
1. Simplified VPIN Calculation:
Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags
This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume)
Impact: May misclassify volume during ranging/choppy markets; works best in directional moves
2. Pseudo-Random Sampling:
Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods
This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close × 100 - floor(close × 100)) + (volume % 100) / 100
Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection
3. Hawkes Process Approximation:
Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: λ(t) = μ + Σ α·exp(-β(t-tᵢ)) fitted via iterative optimization
This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event)
Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments
4. Kyle's Lambda Simplification:
Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: Δp = λ × Δv + ε
This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression
Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals
5. Entropy Calculation:
Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -Σ p(x)·log₂(p(x)) where p(x) is probability of each price change magnitude
This Implementation: Simple ratio of unique price changes to total observations (variety measure)
Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape
6. Memory System Constraints:
Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces
This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores
Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking
7. Limited Feature Engineering:
Advanced Implementation: Dozens of engineered features, polynomial interactions (x², x³), dimensionality reduction (PCA, autoencoders), feature selection algorithms
This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation
Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations
8. Single-Instrument Data:
Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes
This Implementation: Only OHLCV data from displayed instrument
Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets
9. Fixed Performance Horizon:
Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement
This Implementation: Fixed 8-bar evaluation window
Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach
Performance Impact Summary:
These simplifications make the script:
✅ Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations
✅ More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers
✅ More Transparent: All calculations visible in Pine Script (no black-box compiled models)
✅ Lower Resource Usage: <500 bars lookback, minimal memory footprint
⚠️ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations
⚠️ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows
⚠️ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized
When to Upgrade to Full Implementation:
Consider professional Python/C++ versions with institutional data feeds if:
Trading with >$100K capital where precision differences materially impact returns
Operating in microsecond-competitive environments (HFT, market making)
Requiring regulatory-grade audit trails and reproducibility
Backtesting with tick-level precision for strategy validation
Need true real-time adaptation with neural network-based learning
For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logic—multi-agent detection with adaptive selection—remains intact.
Technical Notes
All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap )
VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above)
Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling
No repainting: All calculations use confirmed bar data (no forward-looking)
Maximum lookback: 500 bars (set via max_bars_back parameter)
Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed)
Confidence threshold: Minimum 0.25 (25%) enforced on all signals
Memory arrays: Dynamic sizing with FIFO queue management
Limitations and Disclaimers
Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty.
Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context.
Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms.
Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts.
Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets.
No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss.
Forward-Looking Bias: Performance evaluation uses 8-bar forward window—this creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics.
Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes.
Recommended Settings
Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning)
Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices)
Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable)
Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts
Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss
Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens)
Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0)
After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent
Alert Configuration
The script includes built-in alert conditions:
Long Signal: Fires when validated long entry confirmed
Short Signal: Fires when validated short entry confirmed
Alerts fire once per bar (after confirmation requirements met)
Set alert to "Once Per Bar Close" for reliability
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
