Oszillatoren
SP - MACD with DivergenceIdentifies Bullish and Bearish Divergences on any time frame.
Using with a combination of candlestick and a trend bias based on long term trend of the underlying can pic reversals quiet effectively.
Market Extreme Zones IndexThe Market Extreme Zones Index is a new mean reversion (valuation) tool focused on catching long term oversold/overbought zones. Combining an enhanced RSI with a smoothed Z-score this indicator allows traders to find oppurtunities during highly oversold/overbought zones.
I will separate the explanation into the following parts:
1. How does it work?
2. Methodologies & Concepts
3. Use cases
How does it work?
The indicator attempts to catch highly unprobable events in either direction to capture reversal points over the long term. This is done by calculating the Z-Score of an enhanced RSI.
First we need to calculate the Enhanced RSI:
For this we need to calculate 2 additional lengths:
Length1 = user defined length
Length2 = Length1/2
Length3 = √Length
Now we need to calculate 3 different RSIs:
1st RSI => uses classic user defined source and classic user defined length.
2nd RSI => uses classic user defined source and Length 2.
3rd RSI => uses RSI 2 as source and Length 2
Now calculate the divergence:
RSI_base => 2nd RSI * 3 - 1st RSI - 3rd RSI
After this we need to calculate the median of the RSI_base over √Length and make a divergence of these 2:
RSI => RSI_base*2 - median
All that remains now is the Z-score calculations:
We need:
Average RSI value
Standard Deviation = a measure of how dispersed or spread out a set of data values are from their average
Z-score = (Current Value - Average Value) / Standard Deviation
After this we just smooth the Z-score with a Weighted Moving average with √Length
Methodology & Concepts
Mean Reversion Methodology:
The methodology behind mean reversion is the theory that asset prices will eventually return to their long-term average after deviating significantly, driven by the belief that extreme moves are temporary.
Z-Score Methodology:
A Z-score, or standard score, is a statistical measure that indicates how many standard deviations a data point is from the mean of a dataset. A positive z-score means the value is above the mean, a negative score means it's below, and a score of zero means the value is equal to the mean.
You might already be able to see where I am going with this:
Z-Score could be used for the extreme moves to capture reversal points.
By applying it to the RSI rather than the Price, we get a more accurate measurement that allow us to get a banger indicator.
Use Cases
Capturing reversal points
Trend Direction
- while the main use it for mean reversion, the values can indicate whether we are in an uptrend or a downtrend.
Advantages:
Visualization:
The indicator has many plots to ensure users can easily see what the indicator signals, such as highlighting extreme conditions with background colors.
Versatility:
This indicator works across multiple assets, including the S&P500 and more, so it is not only for crypto.
Final note:
No indicator alone is perfect.
Backtests are not indicative of future performance.
Hope you enjoy Gs!
Good luck!
Range Oascilator + LessDivergences + MACD+StochRSIRange Oscillator + EMA Filter
Calculates a custom oscillator based on the highest high and lowest low over a chosen period.
Generates BUY signals when the oscillator crosses up from the oversold zone and price is above the EMA.
Generates SELL signals when the oscillator crosses down from the overbought zone and price is below the EMA.
MACD (3‑10‑16 EMA Settings)
Uses fast EMA = 3, slow EMA = 10, signal EMA = 16.
Detects bullish and bearish crossovers.
These crossovers only trigger a single unified buy/sell signal if they coincide with Stochastic RSI being in oversold (for buy) or overbought (for sell) zones.
Stochastic RSI
Standard calculation with %K and %D smoothing.
Defines oversold (<20) and overbought (>80) zones.
Used both for divergence detection and as a filter for MACD signals.
Divergence Detection
RSI Divergence: Price makes a lower low but RSI makes a higher low (bullish), or price makes a higher high but RSI makes a lower high (bearish).
MACD Histogram Divergence: Price makes a lower low but MACD histogram makes a higher low (bullish), or price makes a higher high but MACD histogram makes a lower high (bearish).
Stochastic RSI Divergence: Similar logic applied to %K line.
Divergences are flagged only once per pivot to avoid repetitive signals.
Visuals
EMA plotted on chart.
BUY/SELL signals shown as triangles above/below bars.
Divergences shown as labels (e.g., “RSI BullDiv”, “MACD BearDiv”).
Unified MACD+Stoch RSI signals shown in distinct colors (lime for buy, orange for sell).
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.
Swing Trade BUY/SELL + SCORING +COLOUR FIXBUY/SELL labels now appear with a score (1–3) next to them.
Color coding visually distinguishes signal strength:
BUY → 1 yellow, 2 light green, 3 dark green
SELL → 1 orange, 2 red, 3 burgundy
This allows you to instantly see the signal strength both numerically and visually.
Swing Trade AL/SAT + Güç Derecesi_huğurlu
Weak signal → MACD crossover only.
Moderate signal → MACD crossover + RSI confirmation.
Strong signal → MACD crossover + RSI + Stoch RSI confirmation.
BUY/SELL labels appear on the chart in different colors and sizes.
This way, you can instantly see which signal is more reliable.
Zayıf sinyal → sadece MACD kesişim var
Orta sinyal → MACD kesişim + RSI teyidi.
Güçlü sinyal → MACD kesişim + RSI + Stoch RSI teyidi.
Stochastic RSI - WT Confluence Signal Detectors (TraderDemircan)Description
What This Indicator Does:
This indicator combines two powerful momentum oscillators—WaveTrend and Stochastic RSI—to identify high-probability trading signals through confluence. Instead of relying on a single indicator that may generate false signals, this tool only triggers buy/sell alerts when both oscillators simultaneously confirm extreme market conditions and trend reversals. This confluence approach significantly reduces noise and helps traders focus on the most reliable setups.
Key Features:
Dual-Oscillator Confluence: Generates signals only when both WaveTrend crossovers and Stochastic RSI extreme levels align
Normalized Scale Display: Both oscillators are plotted on a unified -100 to +100 scale for easy visual comparison
Visual Signal Confirmation: Clear intersection points marked with colored circles, plus optional candle coloring at crossover moments
Customizable Thresholds: Adjust overbought/oversold levels for both oscillators to match your trading style and asset volatility
Clean Visual Presentation: Optional area fill showing WaveTrend momentum difference, making divergences easier to spot
How It Works:
The indicator operates on a confluence principle where multiple conditions must align:
For BUY Signals (Green):
WaveTrend 1 crosses above WaveTrend 2 (bullish crossover)
WaveTrend is in oversold territory (below -53 or -60)
Stochastic RSI K-line is below 20 (oversold)
For SELL Signals (Red):
WaveTrend 1 crosses below WaveTrend 2 (bearish crossover)
WaveTrend is in overbought territory (above 53 or 60)
Stochastic RSI K-line is above 80 (overbought)
WaveTrend Component:
Uses the hlc3 price (average of high, low, close) to calculate a channel index that identifies market momentum waves. The two WaveTrend lines (WT1 and WT2) act similarly to MACD, where crossovers indicate momentum shifts. The oscillator ranges from approximately -100 to +100, with extreme values suggesting potential reversals.
Stochastic RSI Component:
Applies stochastic calculations to RSI values rather than raw price, creating a more sensitive momentum indicator. Values above 80 indicate overbought conditions (potential selling opportunity), while values below 20 indicate oversold conditions (potential buying opportunity). The indicator includes both K-line (faster) and D-line (slower, smoothed) for additional confirmation.
Normalization Technology:
To enable direct visual comparison, the Stochastic RSI (normally 0-100 scale) is normalized to match WaveTrend's -100 to +100 scale. This allows traders to see both oscillators' movements in relation to the same reference levels, making divergences and convergences more apparent.
How to Use:
For Trend Traders:
Wait for confluence signals in the direction of the larger trend
Use buy signals in uptrends as entry points during pullbacks
Use sell signals in downtrends as entry points during bounces
For Reversal Traders:
Focus on confluence signals at major support/resistance levels
Look for divergences between price and oscillators before confluence signals
Consider stronger signals when both oscillators reach extreme levels (WT beyond ±60, Stoch beyond 20/80)
For Scalpers:
Lower the WaveTrend Channel Length (default 10) to 5-7 for more frequent signals
Tighten overbought/oversold thresholds slightly (e.g., WT: ±50, Stoch: 30/70)
Use on lower timeframes (5m, 15m) with strict stop losses
Settings Guide:
WaveTrend Parameters:
Channel Length (10): Controls sensitivity. Lower = more signals but more noise. Higher = fewer but more reliable signals
Average Length (21): Smoothing period for WT2. Higher values reduce whipsaws
Overbought Levels (60/53): Two-tier system. Breaching 60 indicates strong overbought, 53 is moderate
Oversold Levels (-60/-53): Mirror of overbought levels for downside extremes
Stochastic RSI Parameters:
K-Smooth (3): Smoothing for the K-line. Higher = smoother but delayed
D-Smooth (3): Additional smoothing for the D-line signal
RSI Period (14): Standard RSI calculation period
Stoch Period (14): Stochastic calculation lookback
Oversold (20) / Overbought (80): Classic thresholds for extreme conditions
Visual Options:
Show WT Difference Area: Displays the momentum difference between WT1 and WT2 as a blue shaded area
Show WT Intersection Points: Marks crossover points with colored circles (red for bearish, green for bullish)
Color Candles at Intersection: Changes candle colors at crossover moments (blue for bearish, yellow for bullish)
Show Stoch Over Signals: Displays when Stochastic RSI breaches extreme levels
What Makes This Original:
While WaveTrend and Stochastic RSI are established indicators, this script's originality lies in:
Confluence Logic: The specific combination requiring simultaneous confirmation from both oscillators in extreme zones, not just simple crossovers
Normalization Approach: Displaying both oscillators on the same -100 to +100 scale for direct visual comparison, which is not standard
Multi-Tier Overbought/Oversold: Using two levels (60/53) instead of one, allowing for nuanced signal strength assessment
Integrated Visual System: Combining area fills, intersection markers, and candle coloring in a coordinated display that shows momentum flow at a glance
Important Considerations:
This is a momentum-based oscillator system, which performs best in ranging or trending markets with clear swings
In strong trending markets, the oscillator may remain in extreme zones for extended periods (remain overbought during strong uptrends, oversold during strong downtrends)
Confluence signals are intentionally rare to maintain quality—expect fewer signals than with single-indicator systems
Always combine with price action analysis, support/resistance levels, and proper risk management
Not recommended for extremely low volatility or thin markets where oscillators may produce erratic readings
Best Timeframes:
Intraday: 15m, 1H (with tighter parameters)
Swing Trading: 4H, Daily (with default parameters)
Position Trading: Daily, Weekly (with extended Channel Length 15-20)
Typical Use Cases:
Identifying exhaustion points in trending markets
Timing entries during pullbacks in established trends
Spotting potential reversal zones at key price levels
Filtering out weak momentum signals during consolidation
DAO - Demand Advanced Oscillator# DAO - Demand Advanced Oscillator
## 📊 Overview
DAO (Demand Advanced Oscillator) is a powerful momentum oscillator that measures buying and selling pressure by analyzing consecutive high-low relationships. It helps identify market extremes, divergences, and potential trend reversals.
**Values range from 0 to 1:**
- **Above 0.70** = Overbought (potential reversal down)
- **Below 0.30** = Oversold (potential reversal up)
- **0.30 - 0.70** = Neutral zone
---
## ✨ Key Features
✅ **Automatic Divergence Detection**
- Bullish divergences (price lower low + DAO higher low)
- Bearish divergences (price higher high + DAO lower high)
- Visual lines connecting divergence points
✅ **Multi-Timeframe Analysis**
- View higher timeframe DAO on current chart
- Perfect for trend alignment strategies
✅ **Signal Line (EMA)**
- Customizable EMA for trend confirmation
- Crossover signals for momentum shifts
✅ **Real-Time Statistics Dashboard**
- Current DAO value
- Market status (Overbought/Oversold/Neutral)
- Trend direction indicator
✅ **Complete Alert System**
- Overbought/Oversold signals
- Bullish/Bearish divergences
- Signal line crosses
- Level crosses
✅ **Fully Customizable**
- Adjustable periods and levels
- Customizable colors and zones
- Toggle features on/off
---
## 📈 Trading Signals
### 1. Divergences (Most Powerful)
**Bullish Divergence:**
- Price makes lower low
- DAO makes higher low
- Signal: Strong reversal up likely
**Bearish Divergence:**
- Price makes higher high
- DAO makes lower high
- Signal: Strong reversal down likely
### 2. Overbought/Oversold
**Overbought (>0.70):**
- Market may be overextended
- Consider taking profits or looking for shorts
- Can remain overbought in strong trends
**Oversold (<0.30):**
- Market may be oversold
- Consider buying opportunities
- Can remain oversold in strong downtrends
### 3. Signal Line Crossovers
**Bullish Cross:**
- DAO crosses above signal line
- Momentum turning positive
**Bearish Cross:**
- DAO crosses below signal line
- Momentum turning negative
### 4. Level Crosses
**Cross Above 0.30:** Exiting oversold zone (potential uptrend)
**Cross Below 0.70:** Exiting overbought zone (potential downtrend)
---
## ⚙️ Default Settings
📊 Oscillator Period: 14
Number of bars for calculation
📈 Signal Line Period: 9
EMA period for signal line
🔴 Overbought Level: 0.70
Upper threshold
🟢 Oversold Level: 0.30
Lower threshold
🎯 Divergence Detection: ON
Auto divergence identification
⏰ Multi-Timeframe: OFF
Higher TF overlay (optional)
All parameters are fully customizable!
---
## 🔔 Alerts
Six pre-configured alerts available:
1. DAO Overbought
2. DAO Oversold
3. DAO Bullish Divergence
4. DAO Bearish Divergence
5. DAO Signal Cross Up
6. DAO Signal Cross Down
**Setup:** Right-click indicator → Add Alert → Choose condition
---
## 💡 How to Use
### Best Practices:
✅ Focus on divergences (strongest signals)
✅ Combine with support/resistance levels
✅ Use multiple timeframes for confirmation
✅ Wait for price action confirmation
✅ Practice proper risk management
### Avoid:
❌ Trading on indicator alone
❌ Fighting strong trends
❌ Ignoring market context
❌ Overtrading
### Recommended Settings by Trading Style:
**Day Trading:** Period 7-10, All alerts ON
**Swing Trading:** Period 14-21, Divergence alerts
**Scalping:** Period 5-7, Signal crosses
**Position Trading:** Period 21-30, Weekly/Daily TF
---
## 🌍 Markets & Timeframes
**Works on all markets:**
- Forex (all pairs)
- Stocks (all exchanges)
- Cryptocurrencies
- Commodities
- Indices
- Futures
**Works on all timeframes:** 1m to Monthly
---
## 📊 How It Works
DAO calculates the ratio of buying pressure to total market pressure:
1. **Calculate Buying Pressure (DemandMax):**
- If current high > previous high: DemandMax = difference
- Otherwise: DemandMax = 0
2. **Calculate Selling Pressure (DemandMin):**
- If previous low > current low: DemandMin = difference
- Otherwise: DemandMin = 0
3. **Apply Smoothing:**
- Calculate SMA of DemandMax over N periods
- Calculate SMA of DemandMin over N periods
4. **Final Formula:**
```
DAO = SMA(DemandMax) / (SMA(DemandMax) + SMA(DemandMin))
```
This produces a normalized value (0-1) representing market demand strength.
---
## 🎯 Trading Strategies
### Strategy 1: Divergence Trading
- Wait for divergence label
- Confirm at support/resistance
- Enter on confirming candle
- Stop loss beyond recent swing
- Target: opposite level or 0.50
### Strategy 2: Overbought/Oversold
- Best for ranging markets
- Wait for extreme readings
- Enter on reversal from extremes
- Target: middle line (0.50)
### Strategy 3: Trend Following
- Identify trend direction first
- Use DAO to time entries in trend direction only
- Enter on pullbacks to oversold (uptrend) or overbought (downtrend)
- Trade with the trend
### Strategy 4: Multi-Timeframe
- Enable MTF feature
- Trade only when both timeframes align
- Higher TF = trend direction
- Lower TF = precise entry
---
## 📂 Category
**Primary:** Oscillators
**Secondary:** Statistics, Volatility, Momentum
---
## 🏷️ Tags
dao, oscillator, momentum, overbought-oversold, divergence, reversal, demand-indicator, price-exhaustion, statistics, volatility, forex, stocks, crypto, multi-timeframe, technical-analysis
---
## ⚠️ Disclaimer
**This indicator is for educational purposes only.** It does not constitute financial advice. Trading involves substantial risk of loss. Always conduct your own research, use proper risk management, and consult with financial professionals before making trading decisions. Past performance does not guarantee future results.
---
## 📄 License
Open source - Free to use for personal trading, modify as needed, and share with attribution.
---
**Version:** 1.0
**Status:** Production Ready ✅
**Pine Script:** v5
**Trademark-Free:** 100% Safe to Publish
---
*Made with 💙 for traders worldwide*
Hidden Bullish Divergence - B166erThis script will paint a line on the chart when hidden bullish divergence is occurring.
Relative Strength HSIWe add the relative strength indicator. We try to maximize the alpha,
when there is price divergence, we should notice.
Trendlines with Breaks Oscillator [LuxAlgo]The Trendlines with Breaks Oscillator is an oscillator based on the Trendlines with Breaks indicator, and tracks the maximum distance on price from bullish and bearish trendline breakouts.
The oscillator features divergences and trendline breakout detection.
🔶 USAGE
This tool is based on our Trendlines with Breaks indicator, which detects bullish and bearish trendlines and highlights the breaks on the chart. Now, we bring you this tool as an oscillator.
The oscillator calculates the maximum distance between the price and the break of each trendline, for both bullish and bearish cases, then calculates the delta between both.
When the oscillator is above 0, the market is in an uptrend; when it is below 0, it is in a downtrend. An ascending slope indicates positive momentum, and a descending slope indicates negative momentum.
Trendline breaks are displayed as green and red dots on the oscillator. A green dot corresponds to a bullish break of a descending trendline, and a red dot corresponds to a bearish break of an ascending trendline.
The oscillator calculation depends on two parameters from the settings panel: short and long alpha length. These parameters are used to calculate a synthetic EMA with a variable alpha for both bullish and bearish breaks. The final result is the difference between the two averages.
As shown in the image, using the same trend detection parameters but different alphas can produce very different results. The larger the alphas, the smoother the oscillator becomes, detecting bigger trends but making it less reactive.
This tool features the same trendline detection system as the Trendlines with Breaks indicator, which is based on three main parameters: swing length, slope, and calculation method.
As we can see in the image above, the data collected for the oscillator calculation will be different when using different parameters. A larger length detects larger trends. A larger slope or a different calculation method also impacts the final result.
🔹 Signal Line
The signal line is a smoothed version of the oscillator; traders can choose the smoothing method and length used from the settings panel.
In the image, the signal line crossings are displayed as vertical lines. As we can see, the market usually corrects downward after a bearish crossing and corrects upward after a bullish crossing.
Traders can choose among 10 different smoothing methods for the signal line. In the image, we can see how different methods and lengths give different outputs.
🔹 Divergences
The tool features a divergence detector that helps traders understand the strength behind price movements. Traders can adjust the detection length from the settings panel.
As shown in the image, a bearish divergence occurs when the price prints higher highs, but the momentum on the histogram prints lower highs. A bullish divergence occurs when the price prints lower lows, but the histogram prints higher lows.
By adjusting the length of the divergence detector, traders can filter out smaller divergences, allowing the tool to only detect more significant ones.
The image above depicts divergences detected with different lengths; the larger the length, the bigger the divergences are detected.
🔶 SETTINGS
🔹 Trendlines
Swing Detection Lookback: The size of the market structure used for trendline detection.
Slope: Slope steepness, a value of 0 gives horizontal levels, values larger than 1 give a steeper slope
Slope Calculation Method: Choose how the slope is calculated
🔹 Oscillator
Short Alpha Length: Synthetic EMA short period
Long Alpha Length: Synthetic EMA long period
Smoothing Signal: Choose the smoothing method and period
Divergences: Enable or disable divergences and select the detection length.
🔹 Style
Bullish: Select bullish color.
Bearish: Select bearish color.
Squeeze Momentum Early In and Out CandlesJohn Carter presented some candles called "Early In and Out Candles". Although I couldn't imitate the exact candles and warnings I create better indications and bars in my opinion.
When the Candles are above Donchian MA then we have a bullish Momentum.
When the Candles are bellow Donchian MA then we have bearish momentum.
This indicator works best to get an WARNING to enter and close EARLY positions.
Bullish:
When the candles are Light Blue then we have early warning to enter.
When the candles are Dark Blue then we have early warning to close the position.
Bearish:
When the candles are Red then we have early warning to enter.
When the candles are Yellow then we have early warning to close the position.
IMPORTANT NOTES:
Always combine it with the Squeeze Pro indicator.
Suggested Donchian MA: 5 (You can adjust it).
Don't let candles only to be your closing indication once again there are EARLY WARNINGS therefore can move your stop loses to maximize your profits when you are exiting.
I tested my self and I found that is the best strategy when we get Dark Blue candle in the Bullish move I move my stop loss little bit bellow the candle.
Therefore here we go we have early warnings for In and Out.
Thank you and Good Luck.
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.
Stochastic + Bollinger Bands Multi-Timeframe StrategyThis strategy fuses the Stochastic Oscillator from the 4-hour timeframe with Bollinger Bands from the 1-hour timeframe, operating on a 10-hour chart to capture a unique volatility rhythm and temporal alignment discovered through observational alpha.
By blending momentum confirmation from the higher timeframe with short-term volatility extremes, the strategy leverages what some traders refer to as “rotating volatility” — a phenomenon where multi-timeframe oscillations sync to reveal hidden trade opportunities.
🧠 Strategy Logic
✅ Long Entry Condition:
Stochastic on the 4H timeframe:
%K crosses above %D
Both %K and %D are below 20 (oversold zone)
Bollinger Bands on the 1H timeframe:
Price crosses above the lower Bollinger Band, indicating a potential reversal
→ A long trade is opened when both momentum recovery and volatility reversion align.
✅ Long Exit Condition:
Stochastic on the 4H:
%K crosses below %D
Both %K and %D are above 80 (overbought zone)
Bollinger Bands on the 1H:
Price reaches or exceeds the upper Bollinger Band, suggesting exhaustion
→ The long trade is closed when either signal suggests a potential reversal or overextension.
🧬 Temporal Structure & Alpha
This strategy is deployed on a 10-hour chart — a non-standard timeframe that may align more effectively with multi-timeframe mean reversion dynamics.
This subtle adjustment exploits what some traders identify as “temporal drift” — the desynchronization of volatility across timeframes that creates hidden rhythm in price action.
→ For example, Stochastic on 4H (lookback 17) and Bollinger Bands on 1H (lookback 20) may periodically sync around 10H intervals, offering unique alpha windows.
📊 Indicator Components
🔹 Stochastic Oscillator (4H, Length 17)
Detects momentum reversals using %K and %D crossovers
Helps define overbought/oversold zones from a mid-term view
🔹 Bollinger Bands (1H, Length 20, ±2 StdDev)
Measures price volatility using standard deviation around a moving average
Entry occurs near lower band (support), exits near upper band (resistance)
🔹 Multi-Timeframe Logic
Uses request.security() to safely reference 4H and 1H indicators from a 10H chart
Avoids repainting by using closed higher-timeframe candles only
📈 Visualization
A plot selector input allows toggling between:
Stochastic Plot (%K & %D, with overbought/oversold levels)
Bollinger Bands Plot (Upper, Basis, Lower from 1H data)
This helps users visually confirm entry/exit triggers in real time.
🛠 Customization
Fully configurable Stochastic and BB settings
Timeframes are independently adjustable
Strategy settings like position sizing, slippage, and commission are editable
⚠️ Disclaimer
This strategy is intended for educational and informational purposes only.
It does not constitute financial advice or a recommendation to buy or sell any asset.
Market conditions vary, and past performance does not guarantee future results.
Always test any trading strategy in a simulated environment and consult a licensed financial advisor before making real-world investment decisions.
Market Profile Dominance Analyzer# Market Profile Dominance Analyzer
## 📊 OVERVIEW
**Market Profile Dominance Analyzer** is an advanced multi-factor indicator that combines Market Profile methodology with composite dominance scoring to identify buyer and seller strength across higher timeframes. Unlike traditional volume profile indicators that only show volume distribution, or simple buyer/seller indicators that only compare candle colors, this script integrates six distinct analytical components into a unified dominance measurement system.
This indicator helps traders understand **WHO controls the market** by analyzing price position relative to Market Profile key levels (POC, Value Area) combined with volume distribution, momentum, and trend characteristics.
## 🎯 WHAT MAKES THIS ORIGINAL
### **Hybrid Analytical Approach**
This indicator uniquely combines two separate methodologies that are typically analyzed independently:
1. **Market Profile Analysis** - Calculates Point of Control (POC) and Value Area (VA) using volume distribution across price channels on higher timeframes
2. **Multi-Factor Dominance Scoring** - Weights six independent factors to produce a composite dominance index
### **Six-Factor Composite Analysis**
The dominance score integrates:
- Price position relative to POC (equilibrium assessment)
- Price position relative to Value Area boundaries (acceptance/rejection zones)
- Volume imbalance within Value Area (institutional bias detection)
- Price momentum (directional strength)
- Volume trend comparison (participation analysis)
- Normalized Value Area position (precise location within fair value zone)
### **Adaptive Higher Timeframe Integration**
The script features an intelligent auto-selection system that automatically chooses appropriate higher timeframes based on the current chart period, ensuring optimal Market Profile structure regardless of the trading timeframe being analyzed.
## 💡 HOW IT WORKS
### **Market Profile Construction**
The indicator builds a Market Profile structure on a higher timeframe by:
1. **Session Identification** - Detects new higher timeframe sessions using `request.security()` to ensure accurate period boundaries
2. **Data Accumulation** - Stores high, low, and volume data for all bars within the current higher timeframe session
3. **Channel Distribution** - Divides the session's price range into configurable channels (default: 20 rows)
4. **Volume Mapping** - Distributes each bar's volume proportionally across all price channels it touched
### **Key Level Calculation**
**Point of Control (POC)**
- Identifies the price channel with the highest accumulated volume
- Represents the price level where the most trading activity occurred
- Serves as a magnetic level where price often returns
**Value Area (VA)**
- Starts at POC and expands both upward and downward
- Includes channels until reaching the specified percentage of total volume (default: 70%)
- Expansion algorithm compares adjacent volumes and prioritizes the direction with higher activity
- Defines the "fair value" zone where most market participants agreed to trade
### **Dominance Score Formula**
```
Dominance Score = (price_vs_poc × 10) +
(price_vs_va × 5) +
(volume_imbalance × 0.5) +
(price_momentum × 100) +
(volume_trend × 5) +
(va_position × 15)
```
**Component Breakdown:**
- **price_vs_poc**: +1 if above POC, -1 if below (shows which side of equilibrium)
- **price_vs_va**: +2 if above VAH, -2 if below VAL, 0 if inside VA
- **volume_imbalance**: Percentage difference between upper and lower VA volumes
- **price_momentum**: 5-period SMA of price change (directional acceleration)
- **volume_trend**: Compares 5-period vs 20-period volume averages
- **va_position**: Normalized position within Value Area (-1 to +1)
The composite score is then smoothed using EMA with configurable sensitivity to reduce noise while maintaining responsiveness.
### **Market State Determination**
- **BUYERS Dominant**: Smooth dominance > +10 (bullish control)
- **SELLERS Dominant**: Smooth dominance < -10 (bearish control)
- **NEUTRAL**: Between -10 and +10 (balanced market)
## 📈 HOW TO USE THIS INDICATOR
### **Trend Identification**
- **Green background** indicates buyers are in control - look for long opportunities
- **Red background** indicates sellers are in control - look for short opportunities
- **Gray background** indicates neutral market - consider range-bound strategies
### **Signal Interpretation**
**Buy Signals** (green triangle) appear when:
- Dominance crosses above -10 from oversold conditions
- Previous state was not already bullish
- Suggests shift from seller to buyer control
**Sell Signals** (red triangle) appear when:
- Dominance crosses below +10 from overbought conditions
- Previous state was not already bearish
- Suggests shift from buyer to seller control
### **Value Area Context**
Monitor the information table (top-right) to understand market structure:
- **Price vs POC**: Shows if trading above/below equilibrium
- **Volume Imbalance**: Positive values favor buyers, negative favors sellers
- **Market State**: Current dominant force (BUYERS/SELLERS/NEUTRAL)
### **Multi-Timeframe Strategy**
The auto-timeframe feature analyzes higher timeframe structure:
- On 1-minute charts → analyzes 2-hour structure
- On 5-minute charts → analyzes Daily structure
- On 15-minute charts → analyzes Weekly structure
- On Daily charts → analyzes Yearly structure
This higher timeframe context helps avoid counter-trend trades against the dominant force.
### **Confluence Trading**
Strongest signals occur when multiple factors align:
1. Price above VAH + positive volume imbalance + buyers dominant = Strong bullish setup
2. Price below VAL + negative volume imbalance + sellers dominant = Strong bearish setup
3. Price at POC + neutral state = Potential breakout/breakdown pivot
## ⚙️ INPUT PARAMETERS
- **Higher Time Frame**: Select specific HTF or use 'Auto' for intelligent selection
- **Value Area %**: Percentage of volume contained in VA (default: 70%)
- **Show Buy/Sell Signals**: Toggle signal triangles visibility
- **Show Dominance Histogram**: Toggle histogram display
- **Signal Sensitivity**: EMA period for dominance smoothing (1-20, default: 5)
- **Number of Channels**: Market Profile resolution (10-50, default: 20)
- **Color Settings**: Customize buyer, seller, and neutral colors
## 🎨 VISUAL ELEMENTS
- **Histogram**: Shows smoothed dominance score (green = buyers, red = sellers)
- **Zero Line**: Neutral equilibrium reference
- **Overbought/Oversold Lines**: ±50 levels marking extreme dominance
- **Background Color**: Highlights current market state
- **Information Table**: Displays key metrics (state, dominance, POC relationship, volume imbalance, timeframe, bars in session, total volume)
- **Signal Shapes**: Triangle markers for buy/sell signals
## 🔔 ALERTS
The indicator includes three alert conditions:
1. **Buyers Dominate** - Fires on buy signal crossovers
2. **Sellers Dominate** - Fires on sell signal crossovers
3. **Dominance Shift** - Fires when dominance crosses zero line
## 📊 BEST PRACTICES
### **Timeframe Selection**
- **Scalping (1-5min)**: Focus on 2H-4H dominance shifts
- **Day Trading (15-60min)**: Monitor Daily and Weekly structure
- **Swing Trading (4H-Daily)**: Track Weekly and Monthly dominance
### **Confirmation Strategies**
1. **Trend Following**: Enter in direction of dominance above/below ±20
2. **Reversal Trading**: Fade extreme readings beyond ±50 when diverging with price
3. **Breakout Trading**: Look for dominance expansion beyond ±30 with increasing volume
### **Risk Management**
- Avoid trading during NEUTRAL states (dominance between -10 and +10)
- Use POC levels as logical stop-loss placement
- Consider VAH/VAL as profit targets for mean reversion
## ⚠️ LIMITATIONS & WARNINGS
**Data Requirements**
- Requires sufficient historical data on current chart (minimum 100 bars recommended)
- Lower timeframes may show fewer bars per HTF session initially
- More accurate results after several complete HTF sessions have formed
**Not a Standalone System**
- This indicator analyzes market structure and participant control
- Should be combined with price action, support/resistance, and risk management
- Does not guarantee profitable trades - past dominance does not predict future results
**Repainting Characteristics**
- Higher timeframe levels (POC, VAH, VAL) update as new bars form within the session
- Dominance score recalculates with each new bar
- Historical signals remain fixed, but current session data is developing
**Volume Limitations**
- Uses exchange-provided volume data which varies by instrument type
- Forex and some CFDs use tick volume (not actual transaction volume)
- Most accurate on instruments with reliable volume data (stocks, futures, crypto)
## 🔍 TECHNICAL NOTES
**Performance Optimization**
- Uses `max_bars_back=5000` for extended historical analysis
- Efficient array management prevents memory issues
- Automatic cleanup of session data on new period
**Calculation Method**
- Market Profile uses actual volume distribution, not TPO (Time Price Opportunity)
- Value Area expansion follows traditional Market Profile auction theory
- All calculations occur on the chart's current symbol and timeframe
## 📚 EDUCATIONAL VALUE
This indicator helps traders understand:
- How institutional traders use Market Profile to identify fair value
- The relationship between price, volume, and market acceptance
- Multi-factor analysis techniques for assessing market conditions
- The importance of higher timeframe structure in trade planning
## 🎓 RECOMMENDED READING
To better understand the concepts behind this indicator:
- "Mind Over Markets" by James Dalton (Market Profile foundations)
- "Markets in Profile" by James Dalton (Value Area analysis)
- Volume Profile analysis in institutional trading
## 💬 USAGE TERMS
This indicator is provided as an educational and analytical tool. It does not constitute financial advice, investment recommendations, or trading signals. Users are responsible for their own trading decisions and should conduct their own research and due diligence.
Trading involves substantial risk of loss. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
Alpha-Weighted RSIDescription:
The Alpha-Weighted RSI is a next-generation momentum oscillator that redefines the classic RSI by incorporating the mathematical principles of Lévy Flight. This advanced adaptation applies non-linear weighting to price changes, making the indicator more sensitive to significant market moves and less reactive to minor noise. It is designed for traders seeking a clearer, more powerful view of momentum and potential reversal zones.
🔍 Key Features & Innovations:
Lévy Flight Alpha Weighting: At the core of this indicator is the Alpha parameter (1.0-2.0), which controls the sensitivity to price changes.
Lower Alpha (e.g., 1.2): Makes the indicator highly responsive to recent price movements, ideal for capturing early trend shifts.
Higher Alpha (e.g., 1.8): Creates a smoother, more conservative output that filters out noise, focusing on stronger momentum.
Customizable Smoothing: The raw Lévy-RSI is smoothed by a user-selectable moving average (8 MA types supported: SMA, EMA, SMMA, etc.), allowing for further customization of responsiveness.
Intuitive Centered Oscillator: The RSI is centered around a zero line, providing a clean visual separation between bullish and bearish territory.
Dynamic Gradient Zones: Subtle, colour coded gradient fills in the overbought (>+25) and oversold (<-25) regions enhance visual clarity without cluttering the chart.
Modern Histogram Display: Momentum is plotted as a sleek histogram that changes color between bright cyan (bullish) and magenta (bearish) based on its position relative to the zero line.
🎯 How to Use & Interpret:
Zero-Line Crossovers: The most basic signals. A crossover above the zero line indicates building bullish momentum, while a crossover below suggests growing bearish momentum.
Overbought/Oversold Levels: Use the +25/-25 and +35/-35 levels as dynamic zones. A reading above +25 suggests strong bullish momentum (overbought), while a reading below -25 indicates strong bearish momentum (oversold).
Divergence Detection: Look for divergences between the Alpha-Weighted RSI and price action. For example, if price makes a new low but the RSI forms a higher low, it can signal a potential bullish reversal.
Alpha Tuning: Adjust the Alpha parameter to match market volatility. In choppy markets, increase alpha to reduce noise. In trending markets, decrease alpha to become more responsive.
⚙️ Input Parameters:
RSI Settings: Standard RSI inputs for Length and Calculation Source.
Lévy Flight Settings: The crucial Alpha factor for response control.
MA Settings: MA Type and MA Length for smoothing the final output.
By applying Lévy Flight dynamics, this indicator offers a nuanced perspective on momentum, helping you stay ahead of the curve. Feedback is always welcome!
Luminous Glide Momentum Indicator [wjdtks255]This indicator, named "Customized SuperSmoother MA Oscillator," applies a smoothing filter to price data using a SuperSmoother technique to reduce noise and enhance signal clarity. It calculates two moving averages on the smoothed data—a fast and a slow—whose difference forms the oscillator line. A signal line is derived by smoothing the oscillator with another moving average. The histogram visualizes the divergence between the oscillator and signal lines, indicating momentum strength and direction.
How it works
SuperSmoother Filter: Reduces price noise to provide smoother and more reliable signals than raw data.
Fast and Slow Moving Averages: The fast MA reacts quicker to price changes, while the slow MA indicates longer trends.
Oscillator: The difference between the fast and slow MAs signals shifts in momentum.
Signal Line: A smoothed version of the oscillator used to generate crossovers.
Histogram: Displays the distance between the oscillator and signal line, with color changes indicating bullish or bearish momentum.
Trading Strategy
Buy Signal: When the oscillator crosses above the signal line, it suggests increasing upward momentum, signaling a potential buy opportunity.
Sell Signal: When the oscillator crosses below the signal line, it suggests increasing downward momentum, signaling a potential sell opportunity.
Histogram Size and Color: Larger green bars indicate stronger bullish momentum; larger red bars indicate stronger bearish momentum.
Usage Tips
Combine this oscillator with other indicators or price action analysis to confirm trading signals.
Adjust smoothing and moving average lengths according to your trading timeframe and the asset volatility.
Use proper risk management to filter out potential false signals common in oscillators.
EMA Cross + RSI + ADX - Autotrade Strategy V2Overview
A versatile trend-following strategy combining EMA 9/21 crossovers with RSI momentum filtering and optional ADX trend strength confirmation. Designed for both cryptocurrency and traditional futures/options markets with built-in stop loss management and automated position reversals.
Key Features
Multi-Market Compatibility: Works on both crypto futures (Bitcoin, Ethereum) and traditional markets (NIFTY, Bank NIFTY, S&P 500 futures, equity options)
Triple Confirmation System: EMA crossover + RSI filter + ADX strength (optional)
Automated Risk Management: 2% stop loss with wick-touch detection
Position Auto-Reversal: Opposite signals automatically close and reverse positions
Webhook Ready: Six distinct alert messages for automation (Entry Buy/Sell, Close Long/Short, SL Hit Long/Short)
Performance Metrics
NIFTY Futures (15min): 50%+ win rate with ADX filter OFF
Crypto Markets: Requires extensive backtesting before live deployment
Optimal Timeframes: 15-minute to 1-hour charts (patience required for higher timeframes)
Strategy Logic
Entry Signals:
LONG: EMA 9 crosses above EMA 21 + RSI > 55 + ADX > 20 (if enabled)
SHORT: EMA 9 crosses below EMA 21 + RSI < 45 + ADX > 20 (if enabled)
Exit Signals:
Opposite EMA crossover (auto-closes current position)
Stop loss hit at 2% from entry price (tracks candle wicks)
Technical Indicators:
Fast EMA: 9-period (short-term trend)
Slow EMA: 21-period (primary trend)
RSI: 14-period with 55/45 thresholds (momentum confirmation)
ADX: 14-period with 20 threshold (trend strength filter - optional)
Market-Specific Settings
Traditional Markets (NIFTY, Bank NIFTY, S&P Futures, Options)
Recommended Settings:
ADX Filter: Turn OFF (less choppy, cleaner trends)
Timeframe: 15-minute chart
Win Rate: 50%+ on NIFTY Futures
Why No ADX: Traditional markets have more institutional participation and smoother price action, making ADX unnecessary
Cryptocurrency Markets (BTC, ETH, Altcoins)
Recommended Settings:
ADX Filter: Turn ON (ADX > 20)
Timeframe: 15-minute to 1-hour
Extensive backtesting required before live trading
Why ADX: Crypto markets are highly volatile and prone to false breakouts; ADX filters low-quality chop
Best Practices
✅ Backtest thoroughly on your specific instrument and timeframe
✅ Use larger timeframes (1H, 4H) for higher quality signals and better risk/reward
✅ Adjust RSI thresholds based on market volatility (try 52/48 for more signals, 60/40 for fewer but stronger)
✅ Monitor ADX effectiveness - disable for traditional markets, enable for crypto
✅ Proper position sizing - adjust default_qty_value based on your capital and instrument price
✅ Paper trade first - test for 2-4 weeks before risking real capital
Risk Management
Fixed 2% stop loss per trade (adjustable)
Stop loss tracks candle wicks for accurate execution
Positions auto-reverse on opposite signals (no manual intervention needed)
0.075% commission built into backtest (adjust for your broker)
Customization Options
All parameters are adjustable via inputs:
EMA periods (default: 9/21)
RSI length and thresholds (default: 14-period, 55/45 levels)
ADX length and threshold (default: 14-period, 20 threshold)
Stop loss percentage (default: 2%)
Webhook Automation
This strategy includes six distinct alert messages for automated trading:
"Entry Buy" - Long position opened
"Entry Sell" - Short position opened
"Close Long" - Long position closed on opposite crossover
"Close Short" - Short position closed on opposite crossover
"SL Hit Long" - Long stop loss triggered
"SL Hit Short" - Short stop loss triggered
Compatible with Delta Exchange, Binance Futures, 3Commas, Alertatron, and other webhook platforms.
Important Notes
⚠️ Crypto markets require extensive backtesting - volatility patterns differ significantly from traditional markets
⚠️ Higher timeframes = better results - 15min works but 1H/4H provide cleaner signals
⚠️ ADX toggle is critical - OFF for traditional markets, ON for crypto
⚠️ Not financial advice - always conduct your own research and use proper risk management
⚠️ Past performance ≠ future results - backtest results may not reflect live trading conditions
Disclaimer
This strategy is for educational and informational purposes only. Trading futures and options involves substantial risk of loss. Always backtest thoroughly, start with paper trading, and never risk more than you can afford to lose. The author assumes no responsibility for any trading losses incurred using this strategy.
3-Minute RSI and EMA Crossover Strategy 3-Minute RSI and EMA Crossover Sell Strategy with Exit Conditions and Re-entry
ADX FAST and NOICE FREE DIThis tool is designed to identify trend strength and direction earlier than the traditional ADX/DI system.
Instead of relying on the normal Wilder smoothing, this version applies momentum projection to ADX (Fast ADX)
and then filters all directional movement signals through Hull smoothing to minimize market noise.
The result:
• Trends are detected faster
• Pullbacks are filtered more cleanly
• Sideways or weak structures become easy to avoid
Recommended Usage:
• Look for Fast ADX above the threshold to confirm trend environment
• Use Noise-Free +DI and -DI to confirm trend direction (bullish / bearish dominance)
• Background color highlights only when trend + direction are aligned
This is not a buy/sell signal generator by itself; it is best used as a trend and market condition confirmation layer.
Disclaimer:
This script is provided for educational and informational purposes only.
It does not constitute financial advice or a recommendation to buy or sell any security.
Market conditions vary and past performance does not guarantee future results.
Always perform your own analysis and risk management, and trade responsibly.
Robust Scaled Dema | OquantOverview
The Robust Scaled DEMA indicator is a tool designed for traders seeking to identify potential trend directions in financial markets. It combines the smoothing capabilities of a Double Exponential Moving Average (DEMA) with a robust scaling mechanism to normalize the data, making it more resilient to outliers and extreme price movements. This scaling helps in generating long and short signals based on predefined thresholds, visualized through color-coded plots and bars. The indicator aims to provide a balanced view of market momentum, reducing the impact of noise while highlighting significant shifts in price behavior.
Key Factors/Components
DEMA (Double Exponential Moving Average): Serves as the core smoothing component, reducing lag compared to simple averages by emphasizing recent price action more effectively.
Robust Scaling Mechanism: Utilizes statistical measures like median and interquartile range to normalize the DEMA values, ensuring the indicator is less sensitive to extreme values or price spikes.
Thresholds: User-defined upper and lower levels that trigger long or short signals when the scaled DEMA crosses them.
Visual Elements: Includes plotted lines for the scaled DEMA and thresholds, plus color-coded candlestick bars for intuitive interpretation.
Alerts: Built-in conditions for notifying users of potential entry points for long or short positions.
How It Works
The indicator starts by applying a DEMA to the chosen price source to create a smoothed representation of the market's direction. This smoothed value is then scaled using a robust statistical approach that accounts for the distribution of recent DEMA values, centering it around a median and adjusting for variability to minimize the influence of outliers. The resulting scaled metric is compared against user-set upper and lower thresholds: crossing above the upper suggests a bullish momentum (long signal), while dipping below the lower indicates bearish conditions (short signal). A state variable tracks these conditions to color the chart accordingly, helping traders visualize regime changes. Optional alerts fire on transitions.
For Who Is Best/Recommended Use Cases
This indicator is ideal for traders who employ trend-following or momentum-based strategies and need tools that perform well in non-normal market conditions, such as during high volatility or in assets prone to spikes. Use cases include identifying entry/exit points in trending environments, confirming breakouts, or integrating into multi-indicator systems for added confirmation. Quantitative traders or those backtesting strategies will appreciate its customizable parameters for optimization.
Settings and Default Settings
Source: The price data input for calculations, such as close, open, high, or low. Default: close.
DEMA Length: Controls the period for the DEMA smoothing; shorter values increase responsiveness but may add noise, longer ones provide more lag but smoother signals. Default: 25.
Robust Scaling Length: Defines the lookback period for the scaling statistics; affects how adaptive the normalization is to recent data distributions. Default: 40.
Upper Threshold: The level above which a long signal is triggered; higher values make signals rarer but potentially more reliable. Default: 0.5.
Lower Threshold: The level below which a short signal is triggered; lower values allow for more aggressive bearish detection. Default: 0.
Conclusion
The Robust Scaled DEMA offers an outlier-resistant alternative to traditional moving average indicators, empowering traders to navigate volatile markets. By blending exponential smoothing with statistical robustness, it provides actionable insights into trend shifts while minimizing false positives from extreme events..
⚠️ Disclaimer: This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate indicators/strategies before applying them in live markets. Use at your own risk.
