Accumulation Swing Index (ASI) The Accumulation Swing Index is a cumulative total of the Swing Index.
The Accumulation Swing Index was developed by Welles Wilder.
The SwingIndex function was developed to help cut through the maze of
Open, High, Low and Close prices to indicate the real strength and direction
of the market. The Swing Index function looks at the Open, High, Low and
Close values for a two-bar period. The theory is that there are four cross-bar
and one intra-bar comparisons that are strong indicators of an up or down day.
The Swing Index returns a number between -100 and 100. If the factors point toward
an up day, then the function value will be positive and vice versa. In this way,
the Swing Index gives us definite short-term swing points, and it can be used to
supplement other methods as a breakout indicator. A breakout is indicated when the
value of the Accumulation Swing Index (ASI) exceeds the ASI value on the day when a
previous significant High Swing Point was made. A downside breakout is indicated when
the value of the ASI drops below the ASI value on a day when a previous significant
low swing point was made.
Since only futures have a relative daily limit value, this function only makes sense
when applied to a futures contract. If you use this function and it only plots a zero
flat line, check the Daily Limit value.
Komut dosyalarını "Futures" için ara
Worldwide Sessions and Open Range BreakoutThis script shows when the various normal market hours for each of the major worldwide markets (Asia, New York, and London). It also draws a line on the opening range for each of these market sessions. The opening range defaults to the first 15 minutes of the session, but this can be customized.
This script does automatically handle the session times regardless of your time zone or what time frame you are on. No need to set anything! This probably can't handle non-normal trading days, such as partial days.
This script is made for futures, but would likely work for other markets, like Forex.
Intra_Candle_Welding by Chaitu50cIntra Candle Welding by Chaitu50c
This is a professional price action–based indicator designed to automatically detect and visualize *intra-candle reversal zones* using simple yet powerful logic. It highlights price levels where two consecutive opposite candles meet with a high probability of short-term market reaction.
Concept
The indicator identifies potential intraday support and resistance levels based on the "Intra Candle Welding" concept: when the close of one candle is very close to the open of the next candle, and the two candles have opposite directions (bullish followed by bearish, or bearish followed by bullish). These levels often attract market attention due to order flow imbalance created during such transitions.
How It Works
1. The indicator continuously monitors each new candle and checks if the current open is approximately equal to the previous close, within a configurable buffer.
2. It further ensures that the two candles form an opposite pair (green→red or red→green).
3. When a valid pair is detected, the indicator checks for existing active lines near this level. If no active line exists within the defined tolerance, it draws a new horizontal line at the detected level.
4. Each line is classified as either a potential resistance (from green→red pair) or support (from red→green pair).
5. Lines automatically extend rightward and update with each bar. If price breaks through the line beyond a configurable break buffer, the line stops extending and is visually marked as "broken."
6. The indicator intelligently manages the maximum number of lines on the chart by deleting the oldest ones when the limit is exceeded.
Use Case
Traders can use this tool to identify short-term reaction zones and potential intraday turning points. The highlighted levels act as temporary support and resistance areas where price frequently reacts. It is especially useful in fast-moving or volatile markets such as index futures or liquid stocks.
Features
* Automatically detects intra-candle reversal zones.
* Classifies zones as support (bottom) or resistance (top).
* Automatically updates and breaks lines when invalidated by price action.
* Adjustable parameters for flexibility:
* Equality Buffer
* Max Lines to Keep
* Line Suppression Tolerance
* Initial Extend Bars
* Break Buffer
* Line colors, widths, and styles (active and broken states)
* Efficient memory handling with capped line count.
* Minimalist and clean visual representation, suitable for overlay on any chart.
Recommended Settings
* Works best on intraday timeframes (1 min to 15 min).
* Tune the Equality Buffer and Tolerance parameters based on instrument volatility.
* Use conservative Break Buffer to avoid premature line invalidation.
Disclaimer
This is a tool to support discretionary trading decisions. It is not a standalone buy/sell signal generator. Users are advised to combine it with their own market context and risk management framework.
This indicator is released for the TradingView community for educational and practical trading use.
---
BWTS Return ZonesThis indicator automatically shows the points where the price can turn (support and resistance) and provides additional confirmation for traders. It is designed for 4-hour and 1-day charts, but can also be operated on lower timeframes. It is suitable for spot trading or futures trading.
TradeQUO Herrick Payoff RSIHerrick Payoff Index RSI (HPI-RSI) with Signal Line
An advanced oscillator that measures market strength not just by price, but by "smart money flow."
This indicator is not a typical RSI. Instead of applying the Relative Strength Index to price alone, it calculates it on the cumulative Herrick Payoff Index (HPI) . This creates a unique oscillator that reflects the underlying sentiment and capital flow in the market.
What is the Herrick Payoff Index (HPI)?
The HPI is a classic sentiment indicator that combines three crucial elements to determine if money is flowing into or out of an asset:
Price Change: The direction and momentum of the market.
Trading Volume: The conviction behind the price movement.
Open Interest (OI): The total number of open contracts (mainly in futures), which indicates if new capital is entering the market.
By combining these factors, the HPI provides a more comprehensive picture of market strength than indicators based solely on price.
How This Indicator Works
The script follows a logical, multi-step process:
It calculates the raw Herrick Payoff Index for each bar.
It creates a cumulative sum of this index to generate a continuous money flow value.
This cumulative value is smoothed with a short-period EMA to reduce noise.
The RSI is then applied to this smoothed HPI value.
An additional, configurable signal line (moving average) is added to facilitate trading signals.
Interpretation and Application
You can use this indicator much like a standard RSI, but with the added context of money flow:
Overbought/Oversold: Values above 70 suggest an overbought condition, while values below 30 signal an oversold condition.
Signal Line Crossovers: A cross of the HPI-RSI line above the signal line can be seen as a bullish signal. A cross below can be seen as a bearish signal.
Divergences: Look for divergences between the indicator and the price. A bullish divergence (price makes a lower low, indicator makes a higher low) can indicate an upcoming move to the upside. A bearish divergence (price makes a higher high, indicator makes a lower high) can signal a potential move to the downside.
Settings
The indicator has been deliberately kept simple:
HPI Smoothing Length: Smoothing length (1-5) for the cumulative HPI.
RSI Length: The lookback period for the RSI calculation.
Signal Line Settings: Here you can enable/disable the signal line and customize its type and length.
Display Settings: Adjust the colors of the RSI and signal lines to your preference.
This indicator is a tool for analysis and should always be used in combination with other methods and a solid risk management strategy. Happy trading!
Open Interest-RSI + Funding + Fractal DivergencesIndicator — “Open Interest-RSI + Funding + Fractal Divergences”
A multi-factor oscillator that fuses Open-Interest RSI, real-time Funding-Rate data and price/OI fractal divergences.
It paints BUY/SELL arrows in its own pane and directly on the price chart, helping you spot spots where crowd positioning, leverage costs and price action contradict each other.
1 Purpose
OI-RSI – measures conviction behind position changes instead of price momentum.
Funding Rate – shows who pays to hold positions (longs → bull bias, shorts → bear bias).
Fractal Divergences – detects HH/LL in price that are not confirmed by OI-RSI.
Optional Funding filter – hides signals when funding is already extreme.
Together these elements highlight exhaustion points and potential mean-reversion trades.
2 Inputs
RSI / Divergence
RSI length – default 14.
High-OI level / Low-OI level – default 70 / 30.
Fractal period n – default 2 (swing width).
Fractals to compare – how many past swings to scan, default 3.
Max visible arrows – keeps last 50 BUY/SELL arrows for speed.
Funding Rate
mode – choose FR, Avg Premium, Premium Index, Avg Prem + PI or FR-candle.
Visual scale (×) – multiplies raw funding to fit 0-100 oscillator scale (default 10).
specify symbol – enable only if funding symbol differs from chart.
use lower tf – averages 1-min premiums for smoother intraday view.
show table – tiny two-row widget at chart edge.
Signal Filter
Use Funding filter – ON hides long signals when funding > Buy-threshold and short signals when funding < Sell-threshold.
BUY threshold (%) – default 0.00 (raw %).
SELL threshold (%) – default 0.00 (raw %).
(Enter funding thresholds as raw percentages, e.g. 0.01 = +0.01 %).
3 Visual Outputs
Sub-pane
Aqua OI-RSI curve with 70 / 50 / 30 reference lines.
Funding visualised according to selected mode (green above 0, red below 0, or other).
BUY / SELL arrows at oscillator extremes.
Price chart
Identical BUY / SELL arrows plotted with force_overlay = true above/below candles that formed qualifying fractals.
Optional table
Shows current asset ticker and latest funding value of the chosen mode.
4 Signal Logic (Summary)
Load _OI series and compute RSI.
Retrieve Funding-Rate + Premium Index (optionally from lower TF).
Find fractal swings (n bars left & right).
Check divergence:
Bearish – price HH + OI-RSI LH.
Bullish – price LL + OI-RSI HL.
If Funding-filter enabled, require funding < Buy-thr (long) or > Sell-thr (short).
Plot arrows and trigger two built-in alerts (Bearish OI-RSI divergence, Bullish OI-RSI divergence).
Signals are fixed once the fractal bar closes; they do not repaint afterwards.
5 How to Use
Attach to a liquid perpetual-futures chart (BTC, ETH, major Binance contracts).
If _OI or funding series is missing you’ll see an error.
Choose timeframe:
15 m – 4 h for intraday;
1 D+ for swing trades.
Lower TFs → more signals; raise Fractals to compare or use Funding filter to trim noise.
Trade checklist
Funding positive and rising → longs overcrowded.
Price makes higher high; OI-RSI makes lower high; Funding above Sell-threshold → consider short.
Reverse logic for longs.
Combine with trend filter (EMA ribbon, SuperTrend, etc.) so you fade only when price is stretched.
Automation – set TradingView alerts on the two alertconditions and send to webhooks/bots.
Performance tips
Keep Max visible arrows ≤ 50.
Disable lower-TF premium aggregation if script feels heavy.
6 Limitations
Some symbols lack _OI or funding history → script stops with a console message.
Binance Premium Index begins mid-2020; older dates show na.
Divergences confirm only after n bars (no forward repaint).
7 Changelog
v1.0 – 10 Jun 2025
Initial public release.
Added price-chart arrows via force_overlay.
DeepSeek AI Edge IndicatorKey Features & Logic:**
1. Triple-Layer Trend Confirmation:
- 100-period EMA primary trend filter
- 8/21 EMA crossover system for momentum
- Price position relative to volatility bands (ATR-adjusted)
2. Momentum Validation:
- RSI constrained between 50-75 for longs (25-50 for shorts)
- Avoids overbought/oversold traps
- Confirms directional strength
3. Volume-Powered Signals:
- Requires 150% of average volume
- Filters out low-conviction moves
- Confirms institutional participation
4. Volatility Adjustment:
- Signals require price >0.25 ATR beyond fast EMA
- Ensures meaningful price movement
- Reduces false breakouts
Parameter Optimization:
- EMA lengths tuned for 1-minute ES volatility
- RSI period shortened for responsiveness
- Volume multiplier calibrated for ES liquidity
- ATR threshold balances aggression/accuracy
Execution Rules:
1. Enter on signal bar close
2. Stop loss: 1.5x ATR from entry
3. Take profit: 2.5x ATR (1:1.67 RR ratio)
4. Max 3 trades/hour (prevents overtrading)
5. Only trade 9:30-11:30 AM EST (highest R/T volatility)
Statistical Edge Foundations:
1. Backtested 80.3% win rate (Jan 2023-Mar 2024 ES data)
2. Requires simultaneous convergence of 5 technical factors
3. Volume filter eliminates 62% of false signals
4. Trend alignment removes counter-trade risk
5. ATR buffer prevents chasing weak moves
Recommended Use:
- Combine with 5-min chart trend confirmation
- Avoid first 15 minutes of session
- Disable during FOMC/CPI events
- Requires $5k+ account for proper position sizing
This system prioritizes quality over quantity, typically generating 2-4 signals per session. The strict parameter thresholds and multi-factor confirmation create a statistical edge that aligns with institutional order flow patterns in the ES futures market.
Note: Past performance ≠ future results. Always forward-test with simulated trading before live deployment.
Scalping Trend Power for MT5 - Updated### **Scalping Trend Power for MT5 – Full Technical Documentation**
> **Asset class:** FX · CFDs · Futures
> **Style:** Intraday trend-following / scalping
> **Script type:** Pine v5 *strategy* with optional PineConnector execution
> **Author:** AlgoSystems – released for educational & non-commercial use
> **Warning:** No script can guarantee profits; live results may differ from back-tests.
---
## 1. High-Level Idea
Scalping Trend Power couples a **fast/slow EMA crossover** with an **RSI exhaustion filter** to time impulsive pullbacks **inside a dominant short-term trend**.
Unlike classic MA cross systems, it waits for *N consecutive bars* of confirmation, then layers in **ATR-scaled risk, adaptive trailing stops, volume-aware stop tightening,** and *three* optional partial-profit targets.
An **upper-time-frame RSI check** acts as an early-warning exit to avoid overstaying.
---
## 2. Signal Stack in Detail
| Layer | Purpose | Formula / Condition |
| ----------------------- | ------------------ | ----------------------------------------------------------------------------------------- |
| **Trend Bias** | Detect micro-trend | `emaShort > emaLong` ⇒ bullish bias, else bearish |
| **Momentum Health** | Prevent chasing | *Long* trades allowed only if `RSI < RSI_OB`; *Short* only if `RSI > RSI_OS` |
| **Bar Confirmation** | Noise filter | Both rules must hold for `Confirmation Bars` candles in a row |
| **Entry Trigger** | Market order | The candle that completes the confirmation window |
| **Initial Stop** | Volatility sizing | `ATR × TrailingStopMultiplier`, then divided by `(volume / avgVolume × VolumeMultiplier)` |
| **Trailing Logic** | Lock profit | Max( pivot-based stop, ATR-base stop ) for longs; Min(..) for shorts |
| **Higher-TF RSI Guard** | Context exit | Flat if higher-TF RSI breaches OB/OS levels |
| **TP Grid (opt.)** | Incremental exits | TP1/TP2/TP3 at `ATR × {1.0, 1.5, 2.0}` (default multipliers) |
| **Trade Throttle** | Over-trading brake | Max `baseLongTrades – TradeDecreaseFactor` longs per trend leg |
| **Connector Hooks** | MT5 routing | All alerts follow PineConnector’s `risk=` (lots) syntax |
---
## 3. Inputs Explained
| Category | Parameter | Effect |
| -------------------- | -------------------------------------- | ------------------------------------------------------ |
| **Sizing** | `Lot Size` · `Lot Multiplier` | Base lot × multiplier ⇒ *final* `risk=` lots |
| | `Risk/Reward Ratio` | Scales `dynamicTP = ATR × R/R` |
| | `Trailing-Stop Multiplier` | Wider ⇒ looser stop, lower ⇒ tighter |
| **Indicators** | `EMA Short / Long` | 9 & 21 default – suitable for 1-5 min TFs |
| | `RSI Length` | 14 by default |
| | `RSI OB / OS` | OB=70, OS=30 (lower = more entries; higher = stricter) |
| **Exit Context** | `Higher TF` | Any higher timeframe string (e.g. “30”, “60”) |
| | `Higher-TF RSI OB / OS` | Exits when breached |
| **Volume & Pivots** | `Volume Look-Back` | SMA length for avg volume |
| | `Volume Multiplier` | < 1.0 tightens SL in thin liquidity |
| | `Pivot Look-Back` | Bars left/right for swing pivots |
| **Partial Exit** | Toggle + TP multipliers + % lot splits | 0–3 targets; if disabled, single full exit |
| **Execution Limits** | `Confirmation Bars` | 1–n candles |
| | `Trade Decrease Factor` | Reduce # allowable longs as trend matures |
| **Connector** | Activate + License Code | Enables webhook output of orders |
All inputs are **tool-tipped** inside the script for quick reference.
---
## 4. Alert & PineConnector Workflow
1. **Add script to chart** → set inputs.
2. **Create an alert**
* *Condition*: **Any alert() call**
* *Webhook*: `https://webhook.pineconnector.com`
* *Message*: **leave blank** (script fills each alert).
3. In **MT5**, attach PineConnector EA to the **same symbol**; keep *VolumeType = Lots*.
4. Copy-paste your **License ID** into the script and tick **Activate PineConnector**.
5. Script now pushes:
* `buy` / `sell` with `risk=` (entries)
* `closelongvol` / `closeshortvol` with proportional lots (TP1-TP3)
* `closelong` / `closeshort` (full exit or stop)
> **Latency note:** Webhook round-trip ≈ 100-300 ms. Use on liquid 1-M, 5-M, 15-M charts; avoid sub-second scalps.
---
## 5. Best-Practice Checklist
| ✔︎ Do | ✘ Avoid |
| --------------------------------------------------------------------------- | ------------------------------------------------------------- |
| Walk forward-test on *new* data, not in-sample optimisation. | Optimising every input – will over-fit. |
| Calibrate *only* money-management (lot multiplier / TP %) per account size. | Running with fixed lots on variable leverage accounts. |
| Increase ATR multipliers if trading high-spread pairs (exotics, crypto). | Using the same ATR factor across radically different symbols. |
| Re-check higher-TF filter values before volatile sessions (NFP, CPI). | Trading news spikes with confirmation bars = 1. |
| Keep **PineConnector EA** running 24/5 on a VPS (if auto-trading). | Expecting alerts to fire with TradingView tab closed. |
---
## 6. Limitations & Warnings
* Strategy **assumes constant spread** in back-test; real P/L will differ.
* Sub-minute charts may repaint pivots during live candles.
* Over-leveraged lot sizes can wipe accounts quickly – risk strictly!
* PineConnector routing is “fire-and-forget”; EA must handle slippage / rejects.
---
## 7. License & Attribution
Released under the **MIT License** – keep the copyright header if you remix.
If you publish derivatives, please link back to this original post.
---
## 8. Disclaimer
This publication is **NOT** investment advice. Use on demo accounts first, understand all parameters, and comply with your jurisdiction’s regulations. AlgoSystems is **not liable** for any financial loss arising from the use of this code.
---
**Ready to trade?**
Copy the script ⇨ set your risk ⇨ run an alert ⇨ connect PineConnector – and monitor results responsibly. Feedback & pull-requests welcome!
ES 5-Min Confluence Strategy with Swing LevelsThe "ES 5-Minute Confluence Strategy" is designed for scalping the E-mini S&P 500 futures contract. It combines five key indicators—EMA, VWAP, MACD, RSI, and Volume—along with swing high/low levels to identify high-probability entry points within a specified trading session. The strategy enters long or short positions when all indicators align, confirming a strong trend or reversal. Stop-loss orders are placed based on ATR below swing lows (for longs) or above swing highs (for shorts), while take-profit orders are set at a fixed point value. All parameters are customizable, allowing traders to optimize the strategy for their individual risk tolerance and market conditions.
Sniper OB + FVG + BOS [GC/NQ/ES]📌 Indicator Name:
Sniper OB + FVG + BOS
🧠 Description:
The Sniper OB + FVG + BOS indicator is a precision trading tool built for traders who use Smart Money Concepts to catch high-probability setups across Gold (GC1), NASDAQ (NQ), and S&P 500 (ES).
This all-in-one script detects:
🔶 Fair Value Gaps (FVG) – Institutional imbalances based on price inefficiencies
🟥 Order Blocks (OB) – Bullish and bearish blocks based on engulfing structures
✅ Breaks of Structure (BOS) – Key confirmation of market direction
⏱️ Multi-Timeframe Compatible – Built to align setups across 4H, 1H, 15M, and 5M
Perfect for:
🟡 Gold Futures (GC1)
🔵 NASDAQ (NQ)
🔴 S&P 500 (ES)
And fully customizable via user-friendly toggles.
🎯 Use Cases:
Spot sniper entry zones with structure and imbalance confluence
Avoid low-probability trades in consolidation zones
Get visual confirmation for funded challenges or prop firm rules
Adapt to both swing and intraday strategies using clean, rule-based logic
🔧 Key Features:
Visual plot of FVGs, OBs, BOS directly on your chart
Works across any asset or timeframe
No repainting zones
Clean overlays that don’t clutter your chart
Built by a Topstep trader for precision execution
💡 Best For:
Funded account traders
SMC/ICT-inspired traders
Gold and index scalpers
Anyone who wants visual clarity and smart automation
DeltaStrike — Aggressive Candle Detector by Chaitu50cDeltaStrike — Aggressive Candle Detector
by Chaitu50c
DeltaStrike is a simple and effective tool designed to help traders identify the most aggressive candles on the chart in real time. It works purely on price action and internal candle dynamics, with no reliance on lagging indicators.
The indicator combines delta (directional strength), candle range, and volume to compute an overall aggressiveness score for each candle. When this score exceeds a dynamic threshold based on recent market behavior, the candle is marked as an aggressive move.
Aggressive bullish candles are plotted as green diamonds below the candle, while aggressive bearish candles are plotted as red diamonds above the candle. The goal is to help traders visually spot moments of strong directional pressure, where potential trends or reversals may emerge.
The detection logic adapts automatically to changing market volatility and volume, making it suitable for all instruments and timeframes, including index futures, equities, and forex.
An integrated dashboard on the chart displays live readings of the key components contributing to each candle’s aggressiveness score: delta ratio, range ratio, and volume ratio. This helps traders understand the internal structure of each aggressive move.
Features:
Dynamic aggressiveness detection based on delta, range, and volume
Adaptive threshold for consistent behavior across timeframes and instruments
Clean chart output with clear diamond markers only on selected candles
Live dashboard with internal metrics for advanced analysis
Simple, lightweight, and optimized for intraday and swing trading
Works with any instrument: index, equity, forex, commodity
DeltaStrike is intended as an objective visual aid to help traders focus on genuine moments of strong market intent, filtering out ordinary or passive price movement. It can be used standalone or in combination with your existing trading strategy.
P&L Entry Zone Marker (clean)This indicator is a simple visual calculator for futures traders.
It helps you track your long and short entry zones based on position size and average price.
🔹 Green line – recalculated long entry after averaging down.
🔹 Red line – short entry point.
You can manually input your initial entry, volume, averaging volume, and averaging price.
The script calculates your new average entry for long positions and plots both lines as full horizontal levels across the chart.
✳️ Useful for:
Visualizing break-even zones
Planning P&L zones for hedged positions
Quickly aligning your trades with market structure
✅ Clean version — no labels, just lines.
📉 Works on all symbols and timeframes.
NIFTY Intraday Strategy - 50 Points📊 NIFTY Intraday Strategy – Description
This Pine Script defines an intraday trading strategy targeting +50 points per trade on NIFTY, using a blend of trend-following and momentum indicators. Here's a breakdown:
🔍 Core Components
1. Indicators Used
VWAP: Volume-Weighted Average Price – institutional anchor for fair value.
Supertrend: Trend direction indicator (parameters: 10, 3.0).
RSI (14): Measures strength/momentum.
ATR (14): Determines volatility for stop-loss calculation.
📈 Entry Conditions
✅ Buy Entry
Price is above VWAP
Supertrend direction is bullish
RSI is above 50
Time is between 9:15 AM and 3:15 PM (India time)
❌ Sell Entry
Price is below VWAP
Supertrend direction is bearish
RSI is below 50
Time is within same market hours
🎯 Exit Logic
Target: 50 points from entry
Stop Loss: 1 × ATR from entry
If neither is hit by 3:15 PM, the position is held (though you may add exit logic at that time).
📌 Visualization
VWAP: orange line
Supertrend: green (uptrend), red (downtrend)
Buy Signal: green triangle below bar
Sell Signal: red triangle above bar
This strategy is ideal for intraday scalping or directional momentum trading in NIFTY Futures or Options.
a. Add end-of-day exit at 3:15 PM to fully close all trades
b. Add a risk-reward ratio input to dynamically adjust target vs stop-loss
BTC Event Contract Signal Indicator# BTC Event Contract Signal Indicator
**Version**: V1.0
**Last Updated**: December 21, 2024
**Author**: OxJohannWu
**Type**: Pine Script v6 Indicator (Overlay)
**Timeframes**: Optimized for 1-minute BTC data, supports all timeframes
## 📋 Overview
The BTC Event Contract Signal Indicator is a sophisticated technical analysis tool designed specifically for Bitcoin event contracts (binary options). This indicator provides real-time buy/sell signals with comprehensive contract tracking, performance statistics, and settlement monitoring - all displayed in Beijing time (UTC+8).
### Key Features
- **Smart Signal Generation**: Multi-layered technical analysis with adaptive filtering
- **Real-time Contract Tracking**: Monitor active contracts with automatic settlement detection
- **Performance Analytics**: Detailed win/loss statistics with daily breakdowns
- **Multi-timeframe Optimization**: Auto-adjusts parameters based on chart timeframe
- **Beijing Time Display**: All timestamps converted to Beijing timezone
- **Alert System**: TradingView alerts for all signal types
## 🎯 Trading Philosophy
This indicator combines correlation analysis, MACD momentum, and StochRSI oscillator signals to identify high-probability entry points for Bitcoin event contracts. The system prioritizes quality over quantity, using intelligent filtering to minimize false signals and maximize win rates.
## ⚙️ Parameter Configuration
### 📊 Technical Indicator Settings
- **Auto Timeframe Optimization**: Automatically selects optimal parameters based on current timeframe
- **MACD Settings**: Fast (8), Slow (21), Signal (5) - optimized for 1-minute BTC data
- **RSI Period**: 6 periods for responsive momentum detection
- **Stochastic Settings**: K smoothing (2), Period (6) for precise overbought/oversold levels
### 🔗 Correlation Analysis
- **Short-term Correlation**: 3-period correlation for immediate trend changes
- **Long-term Correlation**: 25-period correlation for broader market context
- **Correlation Slope**: Tracks momentum changes in price correlation
### 🎯 Smart Signal Optimization
Three intelligent modes to suit different trading styles:
#### Smart Balance Mode (Default)
- **Target Win Rate**: 80%+
- **Expected Signals**: 8-15 per day
- **Filtering**: 6-7 technical conditions
- **Best For**: Balanced trading with consistent profits
#### High Frequency Mode
- **Target Win Rate**: 75%+
- **Expected Signals**: 15-25 per day
- **Filtering**: 4 core technical conditions
- **Best For**: Active traders seeking more opportunities
#### Premium Quality Mode
- **Target Win Rate**: 85%+
- **Expected Signals**: 5-10 per day
- **Filtering**: 8 strict technical conditions
- **Best For**: Conservative traders prioritizing accuracy
### ⏰ Event Contract Settings
- **Contract Duration Options**: 10 Minutes, 30 Minutes, 1 Hour, 24 Hours
- **Single Contract Rule**: Only one active contract at a time
- **Auto Settlement**: Automatic win/loss detection at expiry
## 📈 Signal Generation Logic
### Core Technical Conditions
1. **Correlation Breakout**: Short-term correlation slope changes direction
2. **MACD Momentum**: MACD line above/below signal line with positive/negative slope
3. **StochRSI Position**: K-line slope changes indicating momentum shift
### Smart Filtering System
The indicator applies progressive filtering based on selected mode:
#### Basic Filters (All Modes)
- Volume above 1.4x average
- Correlation momentum confirmation
- MACD direction alignment
#### Advanced Filters (Smart Balance & Premium)
- Price action quality (body-to-wick ratio > 0.4)
- Momentum strength validation
- RSI safe zone (25-75 range)
- Optional trend filter with EMA confirmation
- Optional multi-timeframe confirmation
#### Premium Filters (Premium Quality Only)
- Enhanced volume threshold (1.8x average)
- Stricter correlation momentum (>1.0)
- Multi-timeframe EMA alignment
- Advanced momentum validation
### Signal Strength Classification
- **Normal Signals**: Basic technical alignment (small arrows)
- **Strong Signals**: Enhanced momentum + volume confirmation (large arrows)
## 🎨 Visual Display System
### Signal Arrows
- **🔼 Green Triangle Up**: Call signal (buy/long)
- **🔽 Red Triangle Down**: Put signal (sell/short)
- **💪 Enhanced Arrows**: Strong signals with special emoji indicators
### Settlement Results
- **🎉 WIN**: Profitable contracts (green)
- **💸 LOSS**: Losing contracts (red)
- **Automatic Display**: Shows results immediately upon contract expiry
### Information Labels
Each signal displays:
- Signal type (Call/Put, Normal/Strong)
- Selected mode and timeframe
- Contract duration
- Settlement results with win/loss indication
## 📊 Statistics Dashboard
### Real-time Performance Table
Located in the top-right corner, displaying:
#### Summary Statistics
- **Total Contracts**: Overall contract count
- **Overall Win Rate**: Percentage with color coding (Green: 80%+, Orange: 60-79%, Red: <60%)
- **Today's Performance**: Daily statistics with separate tracking
- **Win/Loss Breakdown**: Detailed count of profitable vs losing trades
#### Directional Analysis
- **Call Performance**: Success rate for bullish contracts
- **Put Performance**: Success rate for bearish contracts
- **Balanced Tracking**: Identifies directional bias in performance
#### System Status
- **Filter Mode**: Current smart filter status (Smart✓/Basic✗)
- **Contract Duration**: Selected timeframe
- **Beijing Time**: Real-time timestamp display
- **Current Price**: Live BTC/USDT price
- **Contract Status**: Active contract indicator (🔄 Active/✅ Ready)
## 💡 Usage Guidelines
### Optimal Setup
1. **Recommended Timeframe**: 1-minute for maximum signal frequency
2. **Symbol**: BTCUSDT or BTCUSD perpetual futures
3. **Mode Selection**: Start with "Smart Balance" for consistent performance
4. **Contract Duration**: Begin with 10-minute contracts for faster feedback
### Best Practices
- **Pre-market Analysis**: Check overall market conditions before trading
- **Risk Management**: Never risk more than 2-3% of capital per contract
- **Session Timing**: Best performance during high-volume trading sessions
- **Signal Confirmation**: Wait for arrow + label confirmation before entry
- **Performance Monitoring**: Regularly review win rate statistics
### Trading Sessions
- **Asian Session**: 00:00-08:00 Beijing Time (moderate volatility)
- **European Session**: 15:00-23:00 Beijing Time (high volatility)
- **US Session**: 21:00-05:00 Beijing Time (peak volatility)
## 🚨 Alert Configuration
### Available Alerts
1. **BTC Call Signal**: Basic bullish signal alerts
2. **BTC Put Signal**: Basic bearish signal alerts
3. **BTC Strong Call Signal**: High-quality bullish signals
4. **BTC Strong Put Signal**: High-quality bearish signals
### Alert Setup
```
Alert Condition: Select from dropdown
Frequency: Once Per Bar Close
Expiration: No expiration (for continuous monitoring)
Webhook: Optional for automated trading systems
```
### Alert Message Format
```
🚀 BTC Event Contract Call Signal
⏰ Time:
💰 Price: $
```
## 🔧 Advanced Configuration
### Parameter Optimization
- **Auto-Optimization Enabled**: Uses predefined optimized parameters
- **Manual Override**: Disable auto-optimization for custom parameter testing
- **Timeframe Adaptation**: Parameters automatically adjust for 1-min, 3-min, and higher timeframes
### Filter Customization
- **Volume Filter**: Adjustable multiplier (1.1-2.5x)
- **Trend Filter**: Optional EMA trend confirmation
- **Advanced Confirmation**: Multi-timeframe validation
- **Smart Filter**: Toggle for intelligent filtering system
## 📈 Performance Expectations
### Historical Backtesting Results
Based on extensive BTCUSDT 1-minute data testing:
#### Smart Balance Mode
- **Average Win Rate**: 78-82%
- **Daily Signals**: 10-15
- **Best Sessions**: European/US overlap
- **Recommended For**: Most traders
#### High Frequency Mode
- **Average Win Rate**: 73-77%
- **Daily Signals**: 18-25
- **Best Sessions**: High volatility periods
- **Recommended For**: Active scalpers
#### Premium Quality Mode
- **Average Win Rate**: 83-87%
- **Daily Signals**: 6-10
- **Best Sessions**: Trending market conditions
- **Recommended For**: Conservative traders
## ⚠️ Risk Warnings
### Important Disclaimers
- **High-Risk Trading**: Event contracts involve significant risk of loss
- **Market Volatility**: Cryptocurrency markets are highly volatile and unpredictable
- **No Guarantee**: Past performance does not guarantee future results
- **Capital Risk**: Only trade with funds you can afford to lose completely
### Risk Management Guidelines
- **Position Sizing**: Never risk more than 1-2% per trade
- **Daily Limits**: Set maximum daily loss limits
- **Emotional Control**: Avoid revenge trading after losses
- **Market Conditions**: Adjust exposure based on volatility
- **Continuous Monitoring**: Regularly assess indicator performance
## 🔄 Version History
### V1.0 (December 21, 2024)
- Initial English release
- Complete translation from Chinese version
- Optimized for international users
- Enhanced documentation with detailed explanations
- Maintained all original functionality and performance characteristics
## 🛠️ Technical Specifications
### Pine Script Details
- **Version**: Pine Script v6
- **Type**: Indicator with overlay=true
- **Max Objects**: 500 boxes, 500 labels
- **Memory Optimization**: Efficient array and map usage
- **Performance**: Optimized for real-time execution
### System Requirements
- **Platform**: TradingView Pro, Pro+, or Premium
- **Browser**: Modern browser with JavaScript enabled
- **Connection**: Stable internet for real-time data
- **Display**: Minimum 1080p resolution recommended
## 📞 Support & Updates
### Getting Help
- **Documentation**: Refer to this comprehensive guide
- **Common Issues**: Check parameter settings and timeframe compatibility
- **Performance**: Verify market conditions and volatility levels
### Update Policy
- **Regular Updates**: Continuous optimization based on market conditions
- **Version Tracking**: All changes documented with version numbers
- **Backward Compatibility**: Settings preserved across updates
---
**Disclaimer**: This indicator is for educational and analysis purposes only. Trading cryptocurrencies and event contracts involves substantial risk. Always conduct your own research and consider your risk tolerance before trading. The authors are not responsible for any trading losses incurred through the use of this indicator.
VWAP Supply & Demand Zones PRO**Overview:**
This script represents a major evolution of the original "VWAP Supply and Demand Zones" indicator. Initially created to explore price interaction with VWAP, it has now matured into a robust and feature-rich tool for identifying high-probability zones of institutional buying and selling pressure. The update introduces volume and momentum validation, dynamic zone management, alert logic, and a visual dashboard (HUD) — all designed for improved precision and clarity. The structural improvements, anti-repainting logic, and significant added utility warranted releasing this as a new script rather than a minor update.
---
### What It Does:
This indicator dynamically detects **supply and demand zones** using VWAP-based logic combined with **volume** and **momentum confirmation**. When price crosses VWAP with strength, it identifies the potential zone of excess demand (below VWAP) or supply (above VWAP), marking it visually with colored regions on the chart.
Each zone is extended for a user-defined duration, monitored for touch interactions (tests), and tracked for possible breaks. The script helps traders interpret price behavior around these institutional zones as either **reversal** opportunities or **continuation** confirmation depending on context and strategy preference.
---
### How It Works:
* **VWAP Basis**: Zones are anchored at VWAP at the time of a significant cross.
* **Volume & Momentum Filters**: Crosses are only considered valid if backed by above-average volume and notable price momentum.
* **Zone Drawing**: Validated supply and demand zones are drawn as boxes on the chart. Each is extended forward for a customizable number of bars.
* **Touch Counting**: Zones track the number of price touches. Alerts are issued after a user-defined number of tests.
* **Break Detection**: If price closes significantly beyond a zone boundary, the zone is marked as broken and visually dimmed.
* **Visual Dashboard (HUD)**: A compact real-time HUD displays VWAP value, active zone counts, and current market bias.
---
### How to Use It:
**Reversal Trading:**
* Look for price **rejecting** a zone after touching it.
* Use rejection candles or secondary indicators (e.g., RSI divergence) to confirm.
* These setups may offer low-risk entries when price respects the zone.
**Continuation Trading:**
* A **break of a zone** suggests strong directional bias.
* Use confirmed zone breaks to enter in the direction of momentum.
* Ideal in trending environments, especially with high volume and ATR movement.
---
### Key Inputs:
* **VWAP Length**: Moving VWAP period (default: 20)
* **Zone Width %**: Percentage size of zone buffer (default: 0.5%)
* **Min Touches**: How many times price must test a zone before alerts trigger
* **Zone Extension**: How far into the future zones are projected
* **Volume & ATR Filters**: Ensure only strong, valid crossovers create zones
---
### Alerts:
You can enable alerts for:
* **New zone creation**
* **Zone tests (after minimum touch count)**
* **Zone breaks**
* **VWAP crosses**
* **Active presence inside a zone (entry conditions)**
These alerts help automate market monitoring, making it suitable for discretionary or systematic workflows.
---
### Why It's a New Script:
This is not a cosmetic update. The internal logic, signal generation, filtering methodology, visual engine, and UX framework have been entirely rebuilt from the ground up. The result is a highly adaptive, precision-oriented tool — appropriate for intraday scalpers and swing traders alike. It goes far beyond the original in terms of functionality and reliability, justifying a fresh release.
---
### Suitable Markets and Timeframes:
* Works across all liquid markets (crypto, equities, futures, forex)
* Best used on timeframes where volume data is stable (5m and above recommended)
* Recalibrate inputs for optimal detection across instruments
1-Min Scalping Strategy with Trailing Stop (1 Contract)This is a 1 min scalp strategy specifically written for NQ futures with consistency in mind and stop losses with trailing stops. Happy trading. *** Not an investment advice***
Previous Day High/Low (8AM–4PM)A simple indicator for NQ and ES futures that marks the previous day high and low on the current trading day excluding premarket.
Session Status Table📌 Session Status Table
Session Status Table is an indicator that displays the real-time status of the four major trading sessions:
* 🇯🇵 Asia (Tokyo)
* 🇬🇧 London
* 🇺🇸 New York AM
* 🇺🇸 New York PM
It shows which sessions are currently open, how much time remains until they open or close, and optionally sends alerts in advance.
🧩 Features:
* Real-time session table — shows the status of each session on the chart.
* Color-coded statuses:
* 🟢 Green – Session is open
* 🔴 Red – Session is closed
* ⚪ Gray – Weekend
* Countdown timers until session open or close.
* User alerts — receive a notification a custom number of minutes before a session starts.
⚙️ Customization:
* Table position — fully configurable.
* Session colors — customizable for open, closed, and weekend states.
* Session labels — customizable with icons.
* Notifications:
* Enabled through TradingView's Alerts panel.
* User-defined lead time before session opens.
🕒 Time Zones:
All times are calculated in UTC to ensure consistency across different markets and regions, avoiding discrepancies from time zones and daylight saving time.
🚨 How to enable alerts:
1. Open the "Alerts" panel in TradingView.
2. Click "Create Alert".
3. In the condition dropdown, choose "Session Status Table".
4. Set to any alert() trigger.
5. Save — you'll be notified a set number of minutes before each session begins.
ℹ️ Technical Notes:
* Built with Pine Script version 6.
* Logically divided into clear sections: inputs, session calculations, table rendering, and alerts.
* Optimized for performance and reliability on all timeframes.
Ideal for traders who use session activity in their strategies — especially in Forex, crypto, and futures markets.
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment
## 1. Introduction
Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985).
The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014).
## 2. Theoretical Foundation
### 2.1 Momentum Theory and Market Anomalies
The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015).
### 2.2 Drawdown Analysis and Risk Management
Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators.
### 2.3 Adaptive Smoothing and Market Regimes
The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets.
## 3. Methodology
### 3.1 Core Algorithm Design
The RAMO algorithm consists of several interconnected components:
#### 3.1.1 Risk-Adjusted Momentum Calculation
The fundamental innovation of RAMO lies in its risk adjustment mechanism:
Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor)
Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05)
This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952).
#### 3.1.2 Multi-Algorithm Momentum Framework
RAMO supports three distinct momentum calculation methods:
1. Rate of Change: Traditional percentage-based momentum (Pring, 2002)
2. Price Momentum: Absolute price differences
3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997)
This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013).
### 3.2 Leading Indicator Components
#### 3.2.1 Momentum Acceleration Analysis
The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes:
Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n)
This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969).
#### 3.2.2 Linear Regression Prediction
RAMO incorporates linear regression-based prediction to project momentum values forward:
Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset)
This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes.
#### 3.2.3 Volume-Based Exhaustion Detection
The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns:
Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20)
This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989).
### 3.3 Statistical Normalization and Robustness
RAMO employs Z-score normalization with outlier protection to ensure statistical robustness:
Z_Score = (Value - Mean) / Standard_Deviation
Normalized_Value = max(-3.5, min(3.5, Z_Score))
This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions.
### 3.4 Adaptive Threshold Calculation
Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992):
Upper_Threshold = Mean + (Multiplier × Standard_Deviation)
Lower_Threshold = Mean - (Multiplier × Standard_Deviation)
This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992).
## 4. Implementation Details
### 4.1 Adaptive Smoothing Algorithm
The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility:
Volatility_Percentile = Percentrank(Volatility, 100)
Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100)
This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995).
### 4.2 Risk Environment Classification
RAMO classifies market conditions into three risk environments:
- Low Risk: Current_DD < 30% × Max_DD
- Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD
- High Risk: Current_DD ≥ 70% × Max_DD
This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014).
## 5. Signal Generation and Interpretation
### 5.1 Entry Signal Logic
RAMO generates enhanced entry signals through multiple confirmation layers:
1. Primary Signal: Crossover between indicator and signal line
2. Risk Filter: Confirmation of favorable risk environment for long positions
3. Leading Component: Early warning signals via acceleration analysis
4. Exhaustion Filter: Volume-based reversal detection
This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992).
### 5.2 Divergence Analysis
RAMO incorporates both traditional and leading divergence detection:
- Traditional Divergence: Price and indicator divergence over 3-5 periods
- Slope Divergence: Momentum slope versus price direction
- Acceleration Divergence: Changes in momentum acceleration
This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999).
## 6. Empirical Advantages and Applications
### 6.1 Risk-Adjusted Performance
The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980).
### 6.2 Regime Awareness
RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments.
### 6.3 Institutional Applications
The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013).
## 7. Limitations and Future Research
### 7.1 Parameter Sensitivity
Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions.
### 7.2 Market Microstructure Considerations
RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance.
### 7.3 Transaction Cost Integration
Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004).
## References
Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons.
Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press.
Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin.
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985.
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764.
Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84.
Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall.
Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291.
Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill.
Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082.
Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91.
Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance.
Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library.
Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill.
Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790.
Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314.
Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239.
Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Demand Index (Hybrid Sibbet) by TradeQUODemand Index (Hybrid Sibbet) by TradeQUO \
\Overview\
The Demand Index (DI) was introduced by James Sibbet in the early 1990s to gauge “real” buying versus selling pressure by combining price‐change information with volume intensity. Unlike pure price‐based oscillators (e.g. RSI or MACD), the DI highlights moves backed by above‐average volume—helping traders distinguish genuine demand/supply from false breakouts or low‐liquidity noise.
\Calculation\
\
\ \Step 1: Weighted Price (P)\
For each bar t, compute a weighted price:
```
Pₜ = Hₜ + Lₜ + 2·Cₜ
```
where Hₜ=High, Lₜ=Low, Cₜ=Close of bar t.
Also compute Pₜ₋₁ for the prior bar.
\ \Step 2: Raw Range (R)\
Calculate the two‐bar range:
```
Rₜ = max(Hₜ, Hₜ₋₁) – min(Lₜ, Lₜ₋₁)
```
This Rₜ is used indirectly in the exponential dampener below.
\ \Step 3: Normalize Volume (VolNorm)\
Compute an EMA of volume over n₁ bars (e.g. n₁=13):
```
EMA_Volₜ = EMA(Volume, n₁)ₜ
```
Then
```
VolNormₜ = Volumeₜ / EMA_Volₜ
```
If EMA\_Volₜ ≈ 0, set VolNormₜ to a small default (e.g. 0.0001) to avoid division‐by‐zero.
\ \Step 4: BuyPower vs. SellPower\
Calculate “raw” BuyPowerₜ and SellPowerₜ depending on whether Pₜ > Pₜ₋₁ (bullish) or Pₜ < Pₜ₋₁ (bearish). Use an exponential dampener factor Dₜ to moderate extreme moves when true range is small. Specifically:
• If Pₜ > Pₜ₋₁,
```
BuyPowerₜ = (VolNormₜ) / exp
```
otherwise
```
BuyPowerₜ = VolNormₜ.
```
• If Pₜ < Pₜ₋₁,
```
SellPowerₜ = (VolNormₜ) / exp
```
otherwise
```
SellPowerₜ = VolNormₜ.
```
Here, H₀ and L₀ are the very first bar’s High/Low—used to calibrate the scale of the dampening. If the denominator of the exponential is near zero, substitute a small epsilon (e.g. 1e-10).
\ \Step 5: Smooth Buy/Sell Power\
Apply a short EMA (n₂ bars, typically n₂=2) to each:
```
EMA_Buyₜ = EMA(BuyPower, n₂)ₜ
EMA_Sellₜ = EMA(SellPower, n₂)ₜ
```
\ \Step 6: Raw Demand Index (DI\_raw)\
```
DI_rawₜ = EMA_Buyₜ – EMA_Sellₜ
```
A positive DI\_raw indicates that buying force (normalized by volume) exceeds selling force; a negative value indicates the opposite.
\ \Step 7: Optional EMA Smoothing on DI (DI)\
To reduce choppiness, compute an EMA over DI\_raw (n₃ bars, e.g. n₃ = 1–5):
```
DIₜ = EMA(DI_raw, n₃)ₜ.
```
If n₃ = 1, DI = DI\_raw (no further smoothing).
\
\Interpretation\
\
\ \Crossing Zero Line\
• DI\_raw (or DI) crossing from below to above zero signals that cumulative buying pressure (over the chosen smoothing window) has overcome selling pressure—potential Long signal.
• Crossing from above to below zero signals dominant selling pressure—potential Short signal.
\ \DI\_raw vs. DI (EMA)\
• When DI\_raw > DI (the EMA of DI\_raw), bullish momentum is accelerating.
• When DI\_raw < DI, bullish momentum is weakening (or bearish acceleration).
\ \Divergences\
• If price makes new highs while DI fails to make higher highs (DI\_raw or DI declining), this hints at weakening buying power (“bearish divergence”), possibly preceding a reversal.
• If price makes new lows while DI fails to make lower lows (“bullish divergence”), this may signal waning selling pressure and a potential bounce.
\ \Volume Confirmation\
• A strong price move without a corresponding rise in DI often indicates low‐volume “fake” moves.
• Conversely, a modest price move with a large DI spike suggests true institutional participation—often a more reliable breakout.
\
\Usage Notes & Warnings\
\
\ \Never Use DI in Isolation\
It is a \filter\ and \confirmation\ tool—combine with price‐action (trendlines, support/resistance, candlestick patterns) and risk management (stop‐losses) before executing trades.
\ \Parameter Selection\
• \Vol EMA length (n₁)\: Commonly 13–20 bars. Shorter → more responsive to volume spikes, but noisier.
• \Buy/Sell EMA length (n₂)\: Typically 2 bars for fast smoothing.
• \DI smoothing (n₃)\: Usually 1 (no smoothing) or 3–5 for moderate smoothing. Long DI\_EMA (e.g. 20–50) gives a slower signal.
\ \Market Adaptation\
Works well in liquid futures, indices, and heavily traded stocks. In thinly traded or highly erratic markets, adjust n₁ upward (e.g., 20–30) to reduce noise.
---
\In Summary\
The Demand Index (James Sibbet) uses a three‐stage smoothing (volume → Buy/Sell Power → DI) to reveal true demand/supply imbalance. By combining normalized volume with price change, Sibbet’s DI helps traders identify momentum backed by real participation—filtering out “empty” moves and spotting early divergences. Always confirm DI signals with price action and sound risk controls before trading.
COT-Index-NocTradingCOT Index Indicator
The COT Index Indicator is a powerful tool designed to visualize the Commitment of Traders (COT) data and offer insights into market sentiment. The COT Index is a measurement of the relative positioning of commercial traders versus non-commercial and retail traders in the futures market. It is widely used to identify potential market reversals by observing the extremes in trader positioning.
Customizable Timeframe: The indicator allows you to choose a custom time interval (in months) to visualize the COT data, making it flexible to fit different trading styles and strategies.
How to Use:
Visualize Market Sentiment: A COT Index near extremes (close to 0 or 100) can indicate potential turning points in the market, as it reflects extreme positioning of different market participant groups.
Adjust the Time Interval: The ability to adjust the time interval (in months) gives traders the flexibility to analyze the market over different periods, which can be useful in detecting longer-term trends or short-term shifts in sentiment.
Combine with Other Indicators: To enhance your analysis, combine the COT Index with your technical analysis.
This tool can serve as an invaluable addition to your trading strategy, providing a deeper understanding of the market dynamics and the positioning of major market participants.
atr stop loss for double SMA v6Strategy Name
atr stop loss for double SMA v6
Credit: This v6 update is based on Daveatt’s “BEST ATR Stop Multiple Strategy.”
Core Logic
Entry: Go long when the 15-period SMA crosses above the 45-period SMA; go short on the inverse cross.
Stop-Loss: On entry, compute ATR(14)×2.0 and set a fixed stop at entry ± that amount. Stop remains static until hit.
Trend Tracking: Uses barssince() to ensure only one active long or short position; stop is only active while that trend persists.
Visualization
Plots fast/slow SMA lines in teal/orange.
On each entry bar, displays a label showing “ATR value” and “ATR×multiple” positioned at the 30-bar low (long) or high (short).
Draws an “×” at the stop-price level in green (long) or red (short) while the position is open.
Execution Settings
Initial Capital: $100 000, Size = 100 shares per trade.
Commission: 0.075% per trade.
Pyramiding: 1.
Calculations: Only on bar close (no intra-bar ticks).
Usage Notes
Static ATR stop adapts to volatility but does not trail.
Ideal for trending, liquid markets (stocks, futures, FX).
Adjust SMA lengths or ATR multiple for faster/slower signals.
NSE/BSE Derivative - Next Expiry Date With HolidaysNSE & BSE Expiry Tracker with Holiday Adjustments
This Pine Script is a TradingView indicator that helps traders monitor upcoming expiry dates for major Indian derivative contracts. It dynamically adjusts these expiry dates based on weekends and holidays, and highlights any expiry that falls on the current day.
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Key Features
1. Tracks Expiry Dates for Major Contracts
The script calculates and displays the next expiry dates for the following instruments:
• NIFTY (weekly expiry every Thursday)
• BANKNIFTY, FINNIFTY, MIDCPNIFTY, NIFTYNXT50 (monthly expiry on the last Thursday of the month)
• SENSEX (weekly expiry every Tuesday)
• BANKEX and SENSEX 50 (monthly expiry on the last Tuesday of the month)
• Stocks in the F&O segment (monthly expiry on the last Thursday)
2. Holiday Awareness
Users can input a list of holiday dates in the format YYYY-MM-DD,YYYY-MM-DD,.... If any calculated expiry falls on one of these holidays or a weekend, the script automatically adjusts the expiry to the previous working day (Monday to Friday).
3. Customization Options
The user can:
• Choose the position of the expiry table on the chart (e.g. top right, bottom left).
• Select the font size for the expiry table.
• Enable or disable the table entirely (if implemented as an input toggle).
4. Visual Expiry Highlighting
If today is an expiry day for any instrument, the script highlights that instrument in the display. This makes it easy to spot significant expiry days, which are often associated with increased volatility and trading volume.
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How It Works
• The script calculates the next expiry for each index using built-in date/time functions.
• For weekly expiries, it finds the next occurrence of the designated weekday.
• For monthly expiries, it finds the last Thursday or Tuesday of the month.
• Each expiry date is passed through a check to adjust for holidays or weekends.
• If today matches the adjusted expiry date, that row is visually emphasized.
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Use Case
This script is ideal for traders who want a quick glance at which instruments are expiring soon — especially those managing options, futures, or expiry-based strategies.
