rth vwapPlots the RTH (regular trading hours) VWAP. This is intended for instruments with volume only and mostly for futures. Time zone is set to EST, but start and end times of the VWAP can be configured. Standard setting is set to US equity index futures regular trading hours of 9:30 EST to 16:00 EST.
Поиск скриптов по запросу "Futures"
Liquidation Levels
I got sick of calculating leverage all of the time, so I made this real time calculator. It is primarily for crypto derivatives.
It tracks and displays the liquidation price for 5 customisable leverage levels and plots them either historically and/or in real time, with labels beside each including the estimated price.
These calculations include maintenance margin and can be configured for linear futures (USDT) or non-linear futures. Never again make dumb mistakes that are obvious with a bit of maths.
To jazz it up, you can customise the colours, disable various labels, set different leverage multiples, and change the offsets and number of bars to plot in the past.
Alternatively, you can change the offset to 24 on an hourly chart and change show last bars to 0. By doing this, you can see which levels most often get liquidated. It is crude, I know, and there are better tools for tracking liquidation hunts. This is not an attempt to replace or compete with them.
Enjoy and trade safely.
Bot Analyzer📌 Script Name: Bot Analyzer
This TradingView Pine Script v5 indicator creates a dashboard table on the chart that helps you analyze any asset for running a martingale grid bot on futures.
🔧 User Inputs
TP % (tpPct): Take Profit percentage.
SO step % (soStepPct): Step size between safety orders.
SO n (soCount): Number of safety orders.
M mult (martMult): Martingale multiplier (how much each next order increases in size).
Lev (leverage): Leverage used in futures.
BB len / BB mult: Bollinger Bands settings for measuring channel width.
ATR len: ATR period for volatility.
HV days: Lookback window (days) for Historical Volatility calculation.
📐 Calculations
ATR % (atrPct): Normalized ATR relative to price.
Bollinger Band width % (bbPct): Market channel width as percentage of basis.
Historical Volatility (hvAnn): Annualized volatility, calculated from daily log returns.
Dynamic Step % (dynStepPct): Step size for safety orders, automatically adjusted from ATR and clamped between 0.3% and 5%.
Covered Move % (coveredPct): Total percentage move the bot can withstand before last safety order.
Martingale Size Factor (sizeFactor): Total position size multiplier after all safety orders, based on martingale multiplier.
Risk Score (riskLabel): Simple risk estimate:
Low if risk < 30
Mid if risk < 60
High if risk ≥ 60
📊 Output (Table on Chart)
At the top-right of the chart, the script draws a table with 9 rows:
Metric Value
BB % Bollinger Band width in %
HV % Historical Volatility (annualized %)
TP % Take profit setting
SO step % Safety order step size
SO n Number of safety orders
M mult Martingale multiplier
Dyn step % Dynamic step based on ATR
Size x Total position size factor (e.g., 4.5x)
Risk Risk label (Low / Mid / High)
⚙️ Use Case
Helps choose coins for a martingale bot:
If BB% is wide and HV% is high → the asset is volatile enough.
If Risk shows "High" → parameters are aggressive, you may need to adjust step size, SO count, or leverage.
The dashboard lets you compare assets quickly without switching between multiple indicators.
Smart MACD Volume Trader# Smart MACD Volume Trader
## Overview
Smart MACD Volume Trader is an enhanced momentum indicator that combines the classic MACD (Moving Average Convergence Divergence) oscillator with an intelligent high-volume filter. This combination significantly reduces false signals by ensuring that trading signals are only generated when price momentum is confirmed by substantial volume activity.
The indicator supports over 24 different instruments including major and exotic forex pairs, precious metals (gold and silver), energy commodities (crude oil, natural gas), and industrial metals (copper). For forex and commodity traders, the indicator automatically maps to CME and COMEX futures contracts to provide accurate institutional-grade volume data.
## Originality and Core Concept
Traditional MACD indicators generate signals based solely on price momentum, which can result in numerous false signals during low-activity periods or ranging markets. This indicator addresses this critical weakness by introducing a volume confirmation layer with automatic institutional volume integration.
**What makes this approach original:**
- Signals are triggered only when MACD crossovers coincide with elevated volume activity
- Implements a lookback mechanism to detect volume spikes within recent bars
- Automatically detects and maps 24+ forex pairs and commodities to their corresponding CME and COMEX futures contracts
- Provides real institutional volume data for forex pairs where spot volume is unreliable
- Combines two independent market dimensions (price momentum and volume) into a single, actionable signal
- Includes intelligent asset detection that works across multiple exchanges and ticker formats
**The underlying principle:** Volume validates price movement. When institutional money enters the market, it creates volume signatures. By requiring high volume confirmation and using actual institutional volume data from futures markets, this indicator filters out weak price movements and focuses on trades backed by genuine market participation. The automatic futures mapping ensures that forex and commodity traders always have access to the most accurate volume data available, without manual configuration.
## How It Works
### MACD Component
The indicator calculates MACD using standard methodology:
1. **Fast EMA (default: 12 periods)** - Tracks short-term price momentum
2. **Slow EMA (default: 26 periods)** - Tracks longer-term price momentum
3. **MACD Line** - Difference between Fast EMA and Slow EMA
4. **Signal Line (default: 9-period SMA)** - Smoothed average of MACD line
**Crossover signals:**
- **Bullish:** MACD line crosses above Signal line (momentum turning positive)
- **Bearish:** MACD line crosses below Signal line (momentum turning negative)
### Volume Filter Component
The volume filter adds an essential confirmation layer:
1. **Volume Moving Average** - Calculates exponential MA of volume (default: 20 periods)
2. **High Volume Threshold** - Multiplies MA by ratio (default: 2.0x or 200%)
3. **Volume Detection** - Identifies bars where current volume exceeds threshold
4. **Lookback Period** - Checks if high volume occurred in recent bars (default: 5 bars)
**Signal logic:**
- Buy/Sell signals only trigger when BOTH conditions are met:
- MACD crossover/crossunder occurs
- High volume detected within lookback period
### Automatic CME Futures Integration
For forex traders, spot FX volume data can be unreliable or non-existent. This indicator solves this problem by automatically detecting forex pairs and mapping them to corresponding CME futures contracts with real institutional volume data.
**Supported Major Forex Pairs (7):**
- EURUSD → CME:6E1! (Euro FX Futures)
- GBPUSD → CME:6B1! (British Pound Futures)
- AUDUSD → CME:6A1! (Australian Dollar Futures)
- USDJPY → CME:6J1! (Japanese Yen Futures)
- USDCAD → CME:6C1! (Canadian Dollar Futures)
- USDCHF → CME:6S1! (Swiss Franc Futures)
- NZDUSD → CME:6N1! (New Zealand Dollar Futures)
**Supported Exotic Forex Pairs (4):**
- USDMXN → CME:6M1! (Mexican Peso Futures)
- USDRUB → CME:6R1! (Russian Ruble Futures)
- USDBRL → CME:6L1! (Brazilian Real Futures)
- USDZAR → CME:6Z1! (South African Rand Futures)
**Supported Cross Pairs (6):**
- EURJPY → CME:6E1! (Uses Euro Futures)
- GBPJPY → CME:6B1! (Uses British Pound Futures)
- EURGBP → CME:6E1! (Uses Euro Futures)
- AUDJPY → CME:6A1! (Uses Australian Dollar Futures)
- EURAUD → CME:6E1! (Uses Euro Futures)
- GBPAUD → CME:6B1! (Uses British Pound Futures)
**Supported Precious Metals (2):**
- Gold (XAUUSD, GOLD) → COMEX:GC1! (Gold Futures)
- Silver (XAGUSD, SILVER) → COMEX:SI1! (Silver Futures)
**Supported Energy Commodities (3):**
- WTI Crude Oil (USOIL, WTIUSD) → NYMEX:CL1! (Crude Oil Futures)
- Brent Oil (UKOIL) → NYMEX:BZ1! (Brent Crude Futures)
- Natural Gas (NATGAS) → NYMEX:NG1! (Natural Gas Futures)
**Supported Industrial Metals (1):**
- Copper (COPPER) → COMEX:HG1! (Copper Futures)
**How the automatic detection works:**
The indicator intelligently identifies the asset type by analyzing:
1. Exchange name (FX, OANDA, TVC, COMEX, NYMEX, etc.)
2. Currency pair pattern (6-letter codes like EURUSD, GBPUSD)
3. Commodity identifiers (XAU for gold, XAG for silver, OIL for crude)
When a supported instrument is detected, the indicator automatically switches to the corresponding futures contract for volume analysis. For stocks, cryptocurrencies, and other assets, the indicator uses the native volume data from the current chart.
**Visual feedback:**
An information table appears in the top-right corner of the MACD pane showing:
- Current chart symbol
- Exchange name
- Currency pair or asset name
- Volume source being used (highlighted in orange for futures, yellow for native volume)
- Current high volume status
This provides complete transparency about which data source the indicator is using for its volume analysis.
## How to Use
### Basic Setup
1. Add the indicator to your chart
2. The indicator displays in a separate pane (MACD) and overlay (signals/volume bars)
3. Default settings work well for most assets, but can be customized
### Signal Interpretation
### Visual Signals
**Visual Signals:**
- **Green "BUY" label** - Bullish MACD crossover confirmed by high volume
- **Red "SELL" label** - Bearish MACD crossunder confirmed by high volume
- **Green/Red candles** - Highlight bars with volume exceeding the threshold
- **Light green/red background** - Emphasizes signal bars on the chart
**Information Table:**
A detailed information table appears in the top-right corner of the MACD pane, providing real-time transparency about the indicator's operation:
- **Chart:** Current symbol being analyzed
- **Exchange:** The exchange or data feed being used
- **Pair:** The currency pair or asset name extracted from the ticker
- **Volume From:** The actual symbol used for volume analysis
- Orange color indicates CME or COMEX futures are being used (automatic institutional volume)
- Yellow color indicates native volume from the chart symbol is being used
- Hover tooltip shows whether automatic futures mapping is active
- **High Volume:** Current status showing YES (green) when volume exceeds threshold, NO (gray) otherwise
This table ensures complete transparency and allows you to verify that the correct volume source is being used for your analysis.
**Volume Analysis:**
- Gray histogram bars = Normal volume
- Red histogram bars = High volume (exceeds threshold)
- Green line = Volume moving average baseline
**MACD Analysis:**
- Blue line = MACD line (momentum indicator)
- Orange line = Signal line (trend confirmation)
- Gray dotted line = Zero line (bullish above, bearish below)
### Parameter Customization
**MACD Parameters:**
- Adjust Fast/Slow EMA lengths for different sensitivities
- Shorter periods = More signals, faster response
- Longer periods = Fewer signals, less noise
**Volume Parameters:**
- **Volume MA Period:** Higher values smooth volume analysis
- **High Volume Ratio:** Lower values (1.5x) = More signals; Higher values (3.0x) = Fewer, stronger signals
- **Volume Lookback Bars:** Controls how recent the volume spike must be
**Direction Filters:**
- **Only Buy Signals:** Enables long-only strategy mode
- **Only Sell Signals:** Enables short-only strategy mode
### Alert Configuration
The indicator includes three alert types:
1. **Buy Signal Alert** - Triggers when bullish signal appears
2. **Sell Signal Alert** - Triggers when bearish signal appears
3. **High Volume Alert** - Triggers when volume exceeds threshold
To set up alerts:
1. Click the indicator name → "Add alert on Smart MACD Volume Trader"
2. Select desired alert condition
3. Configure notification method (popup, email, webhook, etc.)
## Trading Strategy Guidelines
### Best Practices
**Recommended markets:**
- Liquid stocks (large-cap, high daily volume)
- Major forex pairs (EURUSD, GBPUSD, USDJPY, AUDUSD, USDCAD, USDCHF, NZDUSD)
- Exotic forex pairs (USDMXN, USDRUB, USDBRL, USDZAR)
- Cross pairs (EURJPY, GBPJPY, EURGBP, AUDJPY, EURAUD, GBPAUD)
- Precious metals (Gold, Silver with automatic COMEX futures mapping)
- Energy commodities (Crude Oil, Natural Gas with automatic NYMEX futures mapping)
- Industrial metals (Copper with automatic COMEX futures mapping)
- Major cryptocurrency pairs
- Index futures and ETFs
**Timeframe recommendations:**
- **Day trading:** 5-minute to 15-minute charts
- **Swing trading:** 1-hour to 4-hour charts
- **Position trading:** Daily charts
**Risk management:**
- Use signals as entry confirmation, not standalone strategy
- Combine with support/resistance levels
- Consider overall market trend direction
- Always use stop-loss orders
### Strategy Examples
**Trend Following Strategy:**
1. Identify overall trend using higher timeframe (e.g., daily chart)
2. Trade only in trend direction
3. Use "Only Buy" filter in uptrends, "Only Sell" in downtrends
4. Enter on signal, exit on opposite signal or at resistance/support
**Volume Breakout Strategy:**
1. Wait for consolidation period (low volume, tight MACD range)
2. Enter when signal appears with high volume (confirms breakout)
3. Target previous swing highs/lows
4. Stop loss below/above recent consolidation
**Forex Scalping Strategy (with automatic CME futures):**
1. The indicator automatically detects forex pairs and uses CME futures volume
2. Trade during active sessions only (use session filter)
3. Focus on quick profits (10-20 pips)
4. Exit at opposite signal or profit target
**Commodities Trading Strategy (Gold, Silver, Oil):**
1. The indicator automatically maps to COMEX and NYMEX futures contracts
2. Trade during high-liquidity sessions (overlap of major markets)
3. Use the high volume confirmation to identify institutional entry points
4. Combine with key support and resistance levels for entries
5. Monitor the information table to confirm futures volume is being used (orange color)
6. Exit on opposite MACD signal or at predefined profit targets
## Why This Combination Works
### The Volume Advantage
Studies consistently show that price movements accompanied by high volume are more likely to continue, while low-volume movements often reverse. This indicator leverages this principle by requiring volume confirmation.
**Key benefits:**
1. **Reduced False Signals:** Eliminates MACD whipsaws during low-volume consolidation
2. **Confirmation Bias:** Two independent indicators (price momentum + volume) agreeing
3. **Institutional Alignment:** High volume often indicates institutional participation
4. **Trend Validation:** Volume confirms that price momentum has "conviction"
### Statistical Edge
By combining two uncorrelated signals (MACD crossovers and volume spikes), the indicator creates a higher-probability setup than either signal alone. The lookback mechanism ensures signals aren't missed if volume spike slightly precedes the MACD cross.
## Supported Exchanges and Automatic Detection
The indicator includes intelligent asset detection that works across multiple exchanges and ticker formats:
**Forex Exchanges (Automatic CME Mapping):**
- FX (TradingView forex feed)
- OANDA
- FXCM
- SAXO
- FOREXCOM
- PEPPERSTONE
- EASYMARKETS
- FX_IDC
**Commodity Exchanges (Automatic COMEX/NYMEX Mapping):**
- TVC (TradingView commodity feed)
- COMEX (directly)
- NYMEX (directly)
- ICEUS
**Other Asset Classes (Native Volume):**
- Stock exchanges (NASDAQ, NYSE, AMEX, etc.)
- Cryptocurrency exchanges (BINANCE, COINBASE, KRAKEN, etc.)
- Index providers (SP, DJ, etc.)
The detection algorithm analyzes three factors:
1. Exchange prefix in the ticker symbol
2. Pattern matching for currency pairs (6-letter codes)
3. Commodity identifiers in the symbol name
This ensures accurate automatic detection regardless of which data feed or exchange you use for charting. The information table in the top-right corner always displays which volume source is being used, providing complete transparency.
## Technical Details
**Calculations:**
- MACD Fast MA: EMA(close, fastLength)
- MACD Slow MA: EMA(close, slowLength)
- MACD Line: Fast MA - Slow MA
- Signal Line: SMA(MACD Line, signalLength)
- Volume MA: Exponential MA of volume
- High Volume: Current volume >= Volume MA × Ratio
**Signal logic:**
```
Buy Signal = (MACD crosses above Signal) AND (High volume in last N bars)
Sell Signal = (MACD crosses below Signal) AND (High volume in last N bars)
```
## Parameters Reference
| Parameter | Default | Description |
|-----------|---------|-------------|
| Volume Symbol | Blank | Manual override for volume source (leave blank for automatic detection) |
| Use CME Futures | False | Legacy option (automatic detection is now built-in) |
| Alert Session | 1530-2200 | Active session time range for alerts |
| Timezone | UTC+1 | Timezone for alert sessions |
| Volume MA Period | 20 | Number of periods for volume moving average |
| High Volume Ratio | 2.0 | Volume threshold multiplier (2.0 = 200% of average) |
| Volume Lookback | 5 | Number of bars to check for high volume confirmation |
| MACD Fast Length | 12 | Fast EMA period for MACD calculation |
| MACD Slow Length | 26 | Slow EMA period for MACD calculation |
| MACD Signal Length | 9 | Signal line SMA period |
| Only Buy | False | Filter to show only bullish signals |
| Only Sell | False | Filter to show only bearish signals |
| Show Signals | True | Display buy and sell labels on chart |
## Optimization Tips
**For volatile markets (crypto, small caps):**
- Increase High Volume Ratio to 2.5-3.0
- Reduce Volume Lookback to 3-4 bars
- Consider faster MACD settings (8, 17, 9)
**For stable markets (large-cap stocks, bonds):**
- Decrease High Volume Ratio to 1.5-1.8
- Increase Volume MA Period to 30-50
- Use standard MACD settings
**For forex (with automatic CME futures):**
- The indicator automatically uses CME futures when forex pairs are detected
- Set appropriate trading session based on your timezone
- Use Volume Lookback of 5-7 bars
- Consider session-based alerts only
- Monitor the information table to verify correct futures mapping
**For commodities (Gold, Silver, Oil, Copper):**
- The indicator automatically maps to COMEX and NYMEX futures
- Increase High Volume Ratio to 2.0-2.5 for metals
- Use slightly higher Volume MA Period (25-30) for smoother analysis
- Trade during active market hours for best volume data
- The information table will show the futures contract being used (orange highlight)
## Limitations and Considerations
**What this indicator does NOT do:**
- Does not predict future price direction
- Does not guarantee profitable trades
- Does not replace proper risk management
- Does not work well in extremely low-volume conditions
**Market conditions to avoid:**
- Pre-market and after-hours sessions (low volume)
- Major news events (volatile, unpredictable volume)
- Holidays and low-liquidity periods
- Extremely low float stocks
## Conclusion
Smart MACD Volume Trader represents a significant evolution of the traditional MACD indicator by combining volume confirmation with automatic institutional volume integration. This dual-confirmation approach significantly improves signal quality by filtering out low-conviction price movements and ensuring traders work with accurate volume data.
The indicator's automatic detection and mapping system supports over 24 instruments across forex, commodities, and metals markets. By intelligently switching to CME and COMEX futures contracts when appropriate, the indicator provides forex and commodity traders with the same quality of volume data that stock traders naturally have access to.
This indicator is particularly valuable for traders who want to:
- Align their entries with institutional money flow
- Avoid getting trapped in false breakouts
- Trade forex pairs with reliable volume data
- Access accurate volume information for gold, silver, and energy commodities
- Combine momentum and volume analysis in a single, streamlined tool
Whether you are day trading stocks, swing trading forex pairs, or positioning in commodities markets, this indicator provides a robust framework for identifying high-probability momentum trades backed by genuine institutional participation. The automatic futures mapping works seamlessly across all supported instruments, requiring no manual configuration or expertise in futures markets.
---
## Support and Updates
This indicator is actively maintained and updated based on user feedback and market conditions. For questions about implementation or custom modifications, please use the comments section below.
**Disclaimer:** This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always conduct your own analysis and risk management before trading.
Antares_messages_publicLibrary "Antares_messages_public"
This library add messages for yours strategy for use in Antares trading system for binance and bybit exchanges.
Данная библиотека позволяет формировать сообщения в алертах стратегий для Antares в более упрощенном для пользователя режиме, включая всплывающие подсказки и т.д.
set_leverage(token, market, ticker_id, leverage)
Set leverage for ticker on specified market.
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
leverage (float) : (float) leverage level. Устанавливаемое плечо.
Returns: 'Set leverage message'.
pause(time_pause)
Set pause in message. '::' -left and '::' -right included.
Parameters:
time_pause (int)
LongLimit(token, market, ticker_id, type_qty, quantity, price, orderId, leverageforqty)
Buy order with limit price and quantity.
Лимитный ордер на покупку(в лонг).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order. Цена по которой должен быть установлен лимитный ордер.
orderId (string) : (string) if use order id you may change or cancel your order after or set it ''. Используйте OrderId если хотите изменить или отменить ордер в будущем.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Limit Buy order'. Лимитный ордер на покупку (лонг).
LongMarket(token, market, ticker_id, type_qty, quantity, leverageforqty)
Market Buy order with quantity.
Рыночный ордер на покупку (в лонг).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
leverageforqty (int) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Market Buy order'. Маркетный ордер на покупку (лонг).
ShortLimit(token, market, ticker_id, type_qty, quantity, price, leverageforqty, orderId)
Sell order with limit price and quantity.
Лимитный ордер на продажу(в шорт).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order. Цена по которой должен быть установлен лимитный ордер.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
orderId (string) : (string) if use order id you may change or cancel your order after or set it ''. Используйте OrderId если хотите изменить или отменить ордер в будущем.
Returns: 'Limit Sell order'. Лимитный ордер на продажу (шорт).
ShortMarket(token, market, ticker_id, type_qty, quantity, leverageforqty)
Sell by market price and quantity.
Рыночный ордер на продажу(в шорт).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
leverageforqty (int) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Market Sell order'. Маркетный ордер на продажу (шорт).
Cancel_by_ticker(token, market, ticker_id)
Cancel all orders for market and ticker in setups. Отменяет все ордера на заданной бирже и заданном токене(паре).
Parameters:
token (string)
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
Returns: 'Cancel all orders'. Отмена всех ордеров на заданной бирже и заданном токене(паре).
Cancel_by_id(token, market, ticker_id, orderId)
Cancel order by Id for market and ticker in setups. Отменяет ордер по Id на заданной бирже и заданном токене(паре).
Parameters:
token (string)
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
orderId (string)
Returns: 'Cancel order'. Отмена ордера по Id на заданной бирже и заданном токене(паре).
Close_positions(token, market, ticker_id)
Close all positions for market and ticker in setups. Закрывает все позиции на заданной бирже и заданном токене(паре).
Parameters:
token (string)
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
Returns: 'Close positions'
CloseLongLimit(token, market, ticker_id, type_qty, quantity, price, orderId, leverageforqty)
Close limit order for long position. (futures)
Лимитный ордер на продажу(в шорт) для закрытия лонговой позиции(reduceonly).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order. Цена по которой должен быть установлен лимитный ордер.
orderId (string) : (string) if use order id you may change or cancel your order after or set it ''. Используйте OrderId если хотите изменить или отменить ордер в будущем.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Limit Sell order reduce only (close long position)'. Лимитный ордер на продажу для снижения текущего лонга(в шорт не входит).
CloseLongMarket(token, market, ticker_id, type_qty, quantity, leverageforqty)
Close market order for long position.
Рыночный ордер на продажу(в шорт) для закрытия лонговой позиции(reduceonly).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Market Sell order reduce only (close long position)'. Ордер на снижение/закрытие текущего лонга(в шорт не входит) по рыночной цене.
CloseShortLimit(token, market, ticker_id, type_qty, quantity, price, orderId, leverageforqty)
Close limit order for short position.
Лимитный ордер на покупку(в лонг) для закрытия шортовой позиции(reduceonly).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order. Цена по которой должен быть установлен лимитный ордер.
orderId (string) : (string) if use order id you may change or cancel your order after or set it ''. Используйте OrderId если хотите изменить или отменить ордер в будущем.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Limit Buy order reduce only (close short position)' . Лимитный ордер на покупку (лонг) для сокращения/закрытия текущего шорта.
CloseShortMarket(token, market, ticker_id, type_qty, quantity, leverageforqty)
Set Close limit order for long position.
Рыночный ордер на покупку(в лонг) для сокращения/закрытия шортовой позиции(reduceonly).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Market Buy order reduce only (close short position)'. Маркетного ордера на покупку (лонг) для сокращения/закрытия текущего шорта.
cancel_all_close(token, market, ticker_id)
Parameters:
token (string)
market (string)
ticker_id (string)
limit_tpsl_bybitfu(token, ticker_id, order_id, side, type_qty, quantity, price, tp_price, sl_price, leverageforqty)
Set multi order for Bybit : limit + takeprofit + stoploss
Выставление тройного ордера на Bybit лимитка со стоплоссом и тейкпрофитом
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
order_id (string)
side (bool) : (bool) "buy side" if true or "sell side" if false. true для лонга, false для шорта.
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order by 'side'. Цена лимитного ордера
tp_price (float) : (float) price for take profit order.
sl_price (float) : (float) price for stoploss order
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: Set multi order for Bybit : limit + takeprofit + stoploss.
replace_limit_tpsl_bybitfu(token, ticker_id, order_id, side, type_qty, quantity, price, tp_price, sl_price, leverageforqty)
Change multi order for Bybit : limit + takeprofit + stoploss
Изменение тройного ордера на Bybit лимитка со стоплоссом и тейкпрофитом
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
order_id (string)
side (bool) : (bool) "buy side" if true or "sell side" if false. true для лонга, false для шорта.
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order by 'side'. Цена лимитного ордера
tp_price (float) : (float) price for take profit order.
sl_price (float) : (float) price for stoploss order
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: Set multi order for Bybit : limit + takeprofit + stoploss.
long_stop(token, market, ticker_id, type_qty, quantity, l_stop, leverageforqty)
Stop market order for long position
Рыночный стоп-ордер на продажу для закрытия лонговой позиции.
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
l_stop (float) : (float) price for activation stop order. Цена активации стоп-ордера.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Stop Market Sell order (close long position)'. Маркетный стоп-ордер на снижения/закрытия текущего лонга.
short_stop(token, market, ticker_id, type_qty, quantity, s_stop, leverageforqty)
Stop market order for short position
Рыночный стоп-ордер на покупку(в лонг) для закрытия шорт позиции.
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
s_stop (float) : (float) price for activation stop order. Цена активации стоп-ордера.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Stop Market Buy order (close short position)'. Маркетный стоп-ордер на снижения/закрытия текущего шорта.
change_stop_l(token, market, ticker_id, type_qty, quantity, l_stop, leverageforqty)
Change Stop market order for long position
Изменяем стоп-ордер на продажу(в шорт) для закрытия лонг позиции.
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
l_stop (float) : (float) price for activation stop order. Цена активации стоп-ордера.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Change Stop Market Buy order (close long position)'. Смещает цену активации Маркетного стоп-ордер на снижения/закрытия текущего лонга.
change_stop_s(token, market, ticker_id, type_qty, quantity, s_stop, leverageforqty)
Change Stop market order for short position
Смещает цену активации Рыночного стоп-ордера на покупку(в лонг) для закрытия шорт позиции.
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string)
quantity (float) : (float) orders size. Размер ордера.
s_stop (float) : (float) price for activation stop order. Цена активации стоп-ордера.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Change Stop Market Buy order (close short position)'. Смещает цену активации Маркетного стоп-ордер на снижения/закрытия текущего шорта.
open_long_position(token, market, ticker_id, type_qty, quantity, l_stop, leverageforqty)
Cancel and close all orders and positions by ticker , then open Long position by market price with stop order
Отменяет все лимитки и закрывает все позы по тикеру, затем открывает лонг по маркету с выставлением стопа (переворот позиции, при необходимости).
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
l_stop (float) : (float). Price for activation stop loss. Цена активации стоп-лосса.
leverageforqty (int) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'command_all_close + LongMarket + long_stop.
open_short_position(token, market, ticker_id, type_qty, quantity, s_stop, leverageforqty)
Cancel and close all orders and positions , then open Short position by market price with stop order
Отменяет все лимитки и закрывает все позы по тикеру, затем открывает шорт по маркету с выставлением стопа(переворот позиции, при необходимости).
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
s_stop (float) : (float). Price for activation stop loss. Цена активации стоп-лосса.
leverageforqty (int) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'command_all_close + ShortMarket + short_stop'.
open_long_trade(token, market, ticker_id, type_qty, quantity, l_stop, qty_ex1, price_ex1, qty_ex2, price_ex2, qty_ex3, price_ex3, leverageforqty)
Cancell and close all orders and positions , then open Long position by market price with stop order and take 1 ,take 2, take 3
Отменяет все лимитки и закрывает все позы по тикеру, затем открывает лонг по маркету с выставлением стопа и 3 тейками (переворот позиции, при необходимости).
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) enter order size, see at type_qty. Размер ордера входа, согласно type_qty.
l_stop (float) : (float). Price for activation stop loss. Цена активации стоп-лосса.
qty_ex1 (float) : (float). Quantity for 1th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 1го тейка, согласно type_qty.. Если 0, то строка для этого тейка не формируется
price_ex1 (float) : (float). Price for 1th take , if = 0 string for order dont set. Цена лимитного ордера для 1го тейка. Если 0, то строка для этого тейка не формируется
qty_ex2 (float) : (float). Quantity for 2th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 2го тейка, согласно type_qty..Если 0, то строка для этого тейка не формируется
price_ex2 (float) : (float). Price for 2th take, if = 0 string for order dont set. Цена лимитного ордера для 2го тейка. Если 0, то строка для этого тейка не формируется
qty_ex3 (float) : (float). Quantity for 3th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 2го тейка, согласно type_qty..Если 0, то строка для этого тейка не формируется
price_ex3 (float) : (float). Price for 3th take, if = 0 string for order dont set. Цена лимитного ордера для 3го тейка. Если 0, то строка для этого тейка не формируется
leverageforqty (int)
Returns: 'cancel_all_close + LongMarket + long_stop + CloseLongLimit1 + CloseLongLimit2+CloseLongLimit3'.
open_short_trade(token, market, ticker_id, type_qty, quantity, s_stop, qty_ex1, price_ex1, qty_ex2, price_ex2, qty_ex3, price_ex3, leverageforqty)
Cancell and close all orders and positions , then open Short position by market price with stop order and take 1 and take 2
Отменяет все лимитки и закрывает все позы по тикеру, затем открывает шорт по маркету с выставлением стопа и 3 тейками (переворот позиции, при необходимости).
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string)
quantity (float)
s_stop (float) : (float). Price for activation stop loss. Цена активации стоп-лосса.
qty_ex1 (float) : (float). Quantity for 1th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 1го тейка, согласно type_qty.. Если 0, то строка для этого тейка не формируется
price_ex1 (float) : (float). Price for 1th take , if = 0 string for order dont set. Цена лимитного ордера для 1го тейка. Если 0, то строка для этого тейка не формируется
qty_ex2 (float) : (float). Quantity for 2th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 2го тейка, согласно type_qty..Если 0, то строка для этого тейка не формируется
price_ex2 (float) : (float). Price for 2th take, if = 0 string for order dont set. Цена лимитного ордера для 2го тейка. Если 0, то строка для этого тейка не формируется
qty_ex3 (float) : (float). Quantity for 3th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 2го тейка, согласно type_qty..Если 0, то строка для этого тейка не формируется
price_ex3 (float) : (float). Price for 3th take, if = 0 string for order dont set. Цена лимитного ордера для 3го тейка. Если 0, то строка для этого тейка не формируется
leverageforqty (int)
Returns: 'command_all_close + ShortMarket + short_stop + CloseShortLimit + CloseShortLimit(2)'.
Multi_LongLimit(token, market, ticker_id, type_qty, qty1, price1, qty2, price2, qty3, price3, qty4, price4, qty5, price5, qty6, price6, qty7, price7, qty8, price8, leverageforqty)
8 or less Buy orders with limit price and quantity.
До 8 Лимитных ордеров на покупку(в лонг).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
qty1 (float)
price1 (float)
qty2 (float)
price2 (float)
qty3 (float)
price3 (float)
qty4 (float)
price4 (float)
qty5 (float)
price5 (float)
qty6 (float)
price6 (float)
qty7 (float)
price7 (float)
qty8 (float)
price8 (float)
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Limit Buy order'. Лимитный ордер на покупку (лонг).
Dual FUT/Spot price with next monthly expiryThis Pine Script dashboard indicator is specifically designed for pair trading strategies in Indian futures markets (NSE). Let me break down how it facilitates pair trading:
Core Pair Trading Concept
The script monitors two correlated stocks simultaneously (Symbol A and Symbol B), comparing their:
Spot prices vs Futures prices
Current month futures vs Next month futures
Premium/discount relationships
Key Pair Trading Features
1. Dual Symbol Monitoring
symbolA = "NSE:TCS" (Default)
symbolB = "NSE:INFY" (Default)
Allows traders to watch two stocks in the same sector (like TCS and Infosys in IT) to identify relative value opportunities.
2. Basis Analysis for Each Stock
The indicator calculates the basis (difference between futures and spot):
Price Difference: FUT - SPOT
Premium/Discount %: ((FUT - SPOT) / SPOT) × 100
This helps identify when one stock's futures are relatively more expensive than the other's.
3. Multi-Expiry View
Near Month Futures (1!): Current active contract
Next Month Futures (2!): Upcoming contract
This enables calendar spread analysis within each stock and helps anticipate rollover effects.
4. Comparative Table
The detailed table displays side-by-side:
Symbol Spot Price Near Future Near Diff (%)Next Monthly Next Diff (%)Lot SizeTCS₹3,500₹3,520+20 (+0.57%)₹3,535+35 (+1.00%)125INFY₹1,450₹1,455+5 (+0.34%)₹1,460+10 (+0.69%)600
5. Lot Size Integration
Critical for position sizing in pair trades - the indicator fetches actual contract lot sizes, enabling proper hedge ratio calculations.
Pair Trading Strategies Enabled
Strategy 1: Basis Divergence Trading
When TCS futures trade at +0.8% premium and INFY at +0.2%
Trade: Short TCS futures, Long INFY futures (betting on convergence)
The indicator highlights these differences with color-coded cells
Strategy 2: Calendar Spread Arbitrage
Compare near month vs next month premium for each stock
If TCS shows wider calendar spread than INFY, potential arbitrage exists
Trade the relative calendar spread difference
Strategy 3: Premium/Discount Reversal
Monitor which stock moves from premium to discount (or vice versa)
Color indicators (green/red) make this immediately visible
Enter pairs when relative premium relationships normalize
Strategy 4: Lot-Adjusted Pair Trading
Use lot size data to create market-neutral positions
Example: If TCS lot = 125 and INFY lot = 600
Ratio = 600/125 = 4.8:1 for rupee-neutral positioning
Visual Trading Cues
Green cells: Futures at premium (contango)
Red cells: Futures at discount (backwardation)
Purple values: Next month contracts
Yellow highlights: Spot prices
Practical Pair Trading Example
Scenario: Both stocks in same sector, historically correlated
Normal state: Both show +0.5% premium
Divergence: TCS jumps to +1.2%, INFY stays at +0.5%
Trade Signal:
Short TCS futures (expensive)
Long INFY futures (relatively cheap)
Exit: When premiums converge back to similar levels
Hedge ratio: Use lot sizes to maintain proper exposure balance
Advantages for Pair Traders
✓ Single-screen monitoring of both legs
✓ Real-time basis calculations eliminate manual math
✓ Multi-timeframe view (near + next month)
✓ Automatic lot size fetching for position sizing
✓ Visual alerts through color coding
✓ Percentage normalization for easy comparison
This indicator essentially transforms raw price data into actionable pair trading intelligence by highlighting relative value discrepancies between correlated assets in the futures market.
Enjoy!!
[KF] Multi-Duration Rate Expectations IndicatorAfter last fed cut in Oct then following jump in rates, I was frustrated at not having access to good rate expectations vs actual because the market usually prices in prior to fed action. This indicator was developed to make futures market rate expectations accessible and interpretable without requiring professional bond analytics systems.
Summary
This Pine Script indicator reveals what the futures market expects for interest rates across three key durations: Fed Funds (overnight), 2-Year, and 10-Year Treasury yields. By comparing futures-implied rates against current spot yields, it provides a clear visual signal of whether the market expects rates to rise, fall, or remain steady.
Understanding Rate Futures
Fed Funds futures (ZQ1!) use a simple design where the expected rate equals 100 minus the futures price. If ZQ1! trades at 96.12, the market expects a 3.88% Fed Funds rate. Treasury futures work differently - they trade as bond prices (typically 102-115) that move inversely to yields. Converting Treasury futures to implied yields requires complex bond mathematics involving duration and conversion factors.
This indicator solves the Treasury futures complexity by implementing a self-calibrating sensitivity model. It observes the historical relationship between futures prices and yields, then uses this to project rate expectations. The model also compares front-month to next-month contracts to detect expected rate direction, automatically adapting as market conditions change.
How to Use
Add the indicator to any chart and select your desired duration in the settings. The display shows the futures-implied rate, current yield, and the difference between them. Green indicates the market expects higher rates, red means lower expectations, and gray shows expectations in line with current rates.
The indicator excels at identifying divergences between market expectations and current rates, which often precede rate movements or futures repricing. Comparing expectations across different durations reveals insights about yield curve positioning and Fed policy anticipation.
Technical Note
While Fed Funds futures provide exact rate expectations, Treasury futures conversions are sophisticated approximations that provide reliable directional signals and reasonable magnitude estimates sufficient for most trading applications.
Kelly Position Size CalculatorThis position sizing calculator implements the Kelly Criterion, developed by John L. Kelly Jr. at Bell Laboratories in 1956, to determine mathematically optimal position sizes for maximizing long-term wealth growth. Unlike arbitrary position sizing methods, this tool provides a scientifically solution based on your strategy's actual performance statistics and incorporates modern refinements from over six decades of academic research.
The Kelly Criterion addresses a fundamental question in capital allocation: "What fraction of capital should be allocated to each opportunity to maximize growth while avoiding ruin?" This question has profound implications for financial markets, where traders and investors constantly face decisions about optimal capital allocation (Van Tharp, 2007).
Theoretical Foundation
The Kelly Criterion for binary outcomes is expressed as f* = (bp - q) / b, where f* represents the optimal fraction of capital to allocate, b denotes the risk-reward ratio, p indicates the probability of success, and q represents the probability of loss (Kelly, 1956). This formula maximizes the expected logarithm of wealth, ensuring maximum long-term growth rate while avoiding the risk of ruin.
The mathematical elegance of Kelly's approach lies in its derivation from information theory. Kelly's original work was motivated by Claude Shannon's information theory (Shannon, 1948), recognizing that maximizing the logarithm of wealth is equivalent to maximizing the rate of information transmission. This connection between information theory and wealth accumulation provides a deep theoretical foundation for optimal position sizing.
The logarithmic utility function underlying the Kelly Criterion naturally embodies several desirable properties for capital management. It exhibits decreasing marginal utility, penalizes large losses more severely than it rewards equivalent gains, and focuses on geometric rather than arithmetic mean returns, which is appropriate for compounding scenarios (Thorp, 2006).
Scientific Implementation
This calculator extends beyond basic Kelly implementation by incorporating state of the art refinements from academic research:
Parameter Uncertainty Adjustment: Following Michaud (1989), the implementation applies Bayesian shrinkage to account for parameter estimation error inherent in small sample sizes. The adjustment formula f_adjusted = f_kelly × confidence_factor + f_conservative × (1 - confidence_factor) addresses the overconfidence bias documented by Baker and McHale (2012), where the confidence factor increases with sample size and the conservative estimate equals 0.25 (quarter Kelly).
Sample Size Confidence: The reliability of Kelly calculations depends critically on sample size. Research by Browne and Whitt (1996) provides theoretical guidance on minimum sample requirements, suggesting that at least 30 independent observations are necessary for meaningful parameter estimates, with 100 or more trades providing reliable estimates for most trading strategies.
Universal Asset Compatibility: The calculator employs intelligent asset detection using TradingView's built-in symbol information, automatically adapting calculations for different asset classes without manual configuration.
ASSET SPECIFIC IMPLEMENTATION
Equity Markets: For stocks and ETFs, position sizing follows the calculation Shares = floor(Kelly Fraction × Account Size / Share Price). This straightforward approach reflects whole share constraints while accommodating fractional share trading capabilities.
Foreign Exchange Markets: Forex markets require lot-based calculations following Lot Size = Kelly Fraction × Account Size / (100,000 × Base Currency Value). The calculator automatically handles major currency pairs with appropriate pip value calculations, following industry standards described by Archer (2010).
Futures Markets: Futures position sizing accounts for leverage and margin requirements through Contracts = floor(Kelly Fraction × Account Size / Margin Requirement). The calculator estimates margin requirements as a percentage of contract notional value, with specific adjustments for micro-futures contracts that have smaller sizes and reduced margin requirements (Kaufman, 2013).
Index and Commodity Markets: These markets combine characteristics of both equity and futures markets. The calculator automatically detects whether instruments are cash-settled or futures-based, applying appropriate sizing methodologies with correct point value calculations.
Risk Management Integration
The calculator integrates sophisticated risk assessment through two primary modes:
Stop Loss Integration: When fixed stop-loss levels are defined, risk calculation follows Risk per Trade = Position Size × Stop Loss Distance. This ensures that the Kelly fraction accounts for actual risk exposure rather than theoretical maximum loss, with stop-loss distance measured in appropriate units for each asset class.
Strategy Drawdown Assessment: For discretionary exit strategies, risk estimation uses maximum historical drawdown through Risk per Trade = Position Value × (Maximum Drawdown / 100). This approach assumes that individual trade losses will not exceed the strategy's historical maximum drawdown, providing a reasonable estimate for strategies with well-defined risk characteristics.
Fractional Kelly Approaches
Pure Kelly sizing can produce substantial volatility, leading many practitioners to adopt fractional Kelly approaches. MacLean, Sanegre, Zhao, and Ziemba (2004) analyze the trade-offs between growth rate and volatility, demonstrating that half-Kelly typically reduces volatility by approximately 75% while sacrificing only 25% of the growth rate.
The calculator provides three primary Kelly modes to accommodate different risk preferences and experience levels. Full Kelly maximizes growth rate while accepting higher volatility, making it suitable for experienced practitioners with strong risk tolerance and robust capital bases. Half Kelly offers a balanced approach popular among professional traders, providing optimal risk-return balance by reducing volatility significantly while maintaining substantial growth potential. Quarter Kelly implements a conservative approach with low volatility, recommended for risk-averse traders or those new to Kelly methodology who prefer gradual introduction to optimal position sizing principles.
Empirical Validation and Performance
Extensive academic research supports the theoretical advantages of Kelly sizing. Hakansson and Ziemba (1995) provide a comprehensive review of Kelly applications in finance, documenting superior long-term performance across various market conditions and asset classes. Estrada (2008) analyzes Kelly performance in international equity markets, finding that Kelly-based strategies consistently outperform fixed position sizing approaches over extended periods across 19 developed markets over a 30-year period.
Several prominent investment firms have successfully implemented Kelly-based position sizing. Pabrai (2007) documents the application of Kelly principles at Berkshire Hathaway, noting Warren Buffett's concentrated portfolio approach aligns closely with Kelly optimal sizing for high-conviction investments. Quantitative hedge funds, including Renaissance Technologies and AQR, have incorporated Kelly-based risk management into their systematic trading strategies.
Practical Implementation Guidelines
Successful Kelly implementation requires systematic application with attention to several critical factors:
Parameter Estimation: Accurate parameter estimation represents the greatest challenge in practical Kelly implementation. Brown (1976) notes that small errors in probability estimates can lead to significant deviations from optimal performance. The calculator addresses this through Bayesian adjustments and confidence measures.
Sample Size Requirements: Users should begin with conservative fractional Kelly approaches until achieving sufficient historical data. Strategies with fewer than 30 trades may produce unreliable Kelly estimates, regardless of adjustments. Full confidence typically requires 100 or more independent trade observations.
Market Regime Considerations: Parameters that accurately describe historical performance may not reflect future market conditions. Ziemba (2003) recommends regular parameter updates and conservative adjustments when market conditions change significantly.
Professional Features and Customization
The calculator provides comprehensive customization options for professional applications:
Multiple Color Schemes: Eight professional color themes (Gold, EdgeTools, Behavioral, Quant, Ocean, Fire, Matrix, Arctic) with dark and light theme compatibility ensure optimal visibility across different trading environments.
Flexible Display Options: Adjustable table size and position accommodate various chart layouts and user preferences, while maintaining analytical depth and clarity.
Comprehensive Results: The results table presents essential information including asset specifications, strategy statistics, Kelly calculations, sample confidence measures, position values, risk assessments, and final position sizes in appropriate units for each asset class.
Limitations and Considerations
Like any analytical tool, the Kelly Criterion has important limitations that users must understand:
Stationarity Assumption: The Kelly Criterion assumes that historical strategy statistics represent future performance characteristics. Non-stationary market conditions may invalidate this assumption, as noted by Lo and MacKinlay (1999).
Independence Requirement: Each trade should be independent to avoid correlation effects. Many trading strategies exhibit serial correlation in returns, which can affect optimal position sizing and may require adjustments for portfolio applications.
Parameter Sensitivity: Kelly calculations are sensitive to parameter accuracy. Regular calibration and conservative approaches are essential when parameter uncertainty is high.
Transaction Costs: The implementation incorporates user-defined transaction costs but assumes these remain constant across different position sizes and market conditions, following Ziemba (2003).
Advanced Applications and Extensions
Multi-Asset Portfolio Considerations: While this calculator optimizes individual position sizes, portfolio-level applications require additional considerations for correlation effects and aggregate risk management. Simplified portfolio approaches include treating positions independently with correlation adjustments.
Behavioral Factors: Behavioral finance research reveals systematic biases that can interfere with Kelly implementation. Kahneman and Tversky (1979) document loss aversion, overconfidence, and other cognitive biases that lead traders to deviate from optimal strategies. Successful implementation requires disciplined adherence to calculated recommendations.
Time-Varying Parameters: Advanced implementations may incorporate time-varying parameter models that adjust Kelly recommendations based on changing market conditions, though these require sophisticated econometric techniques and substantial computational resources.
Comprehensive Usage Instructions and Practical Examples
Implementation begins with loading the calculator on your desired trading instrument's chart. The system automatically detects asset type across stocks, forex, futures, and cryptocurrency markets while extracting current price information. Navigation to the indicator settings allows input of your specific strategy parameters.
Strategy statistics configuration requires careful attention to several key metrics. The win rate should be calculated from your backtest results using the formula of winning trades divided by total trades multiplied by 100. Average win represents the sum of all profitable trades divided by the number of winning trades, while average loss calculates the sum of all losing trades divided by the number of losing trades, entered as a positive number. The total historical trades parameter requires the complete number of trades in your backtest, with a minimum of 30 trades recommended for basic functionality and 100 or more trades optimal for statistical reliability. Account size should reflect your available trading capital, specifically the risk capital allocated for trading rather than total net worth.
Risk management configuration adapts to your specific trading approach. The stop loss setting should be enabled if you employ fixed stop-loss exits, with the stop loss distance specified in appropriate units depending on the asset class. For stocks, this distance is measured in dollars, for forex in pips, and for futures in ticks. When stop losses are not used, the maximum strategy drawdown percentage from your backtest provides the risk assessment baseline. Kelly mode selection offers three primary approaches: Full Kelly for aggressive growth with higher volatility suitable for experienced practitioners, Half Kelly for balanced risk-return optimization popular among professional traders, and Quarter Kelly for conservative approaches with reduced volatility.
Display customization ensures optimal integration with your trading environment. Eight professional color themes provide optimization for different chart backgrounds and personal preferences. Table position selection allows optimal placement within your chart layout, while table size adjustment ensures readability across different screen resolutions and viewing preferences.
Detailed Practical Examples
Example 1: SPY Swing Trading Strategy
Consider a professionally developed swing trading strategy for SPY (S&P 500 ETF) with backtesting results spanning 166 total trades. The strategy achieved 110 winning trades, representing a 66.3% win rate, with an average winning trade of $2,200 and average losing trade of $862. The maximum drawdown reached 31.4% during the testing period, and the available trading capital amounts to $25,000. This strategy employs discretionary exits without fixed stop losses.
Implementation requires loading the calculator on the SPY daily chart and configuring the parameters accordingly. The win rate input receives 66.3, while average win and loss inputs receive 2200 and 862 respectively. Total historical trades input requires 166, with account size set to 25000. The stop loss function remains disabled due to the discretionary exit approach, with maximum strategy drawdown set to 31.4%. Half Kelly mode provides the optimal balance between growth and risk management for this application.
The calculator generates several key outputs for this scenario. The risk-reward ratio calculates automatically to 2.55, while the Kelly fraction reaches approximately 53% before scientific adjustments. Sample confidence achieves 100% given the 166 trades providing high statistical confidence. The recommended position settles at approximately 27% after Half Kelly and Bayesian adjustment factors. Position value reaches approximately $6,750, translating to 16 shares at a $420 SPY price. Risk per trade amounts to approximately $2,110, representing 31.4% of position value, with expected value per trade reaching approximately $1,466. This recommendation represents the mathematically optimal balance between growth potential and risk management for this specific strategy profile.
Example 2: EURUSD Day Trading with Stop Losses
A high-frequency EURUSD day trading strategy demonstrates different parameter requirements compared to swing trading approaches. This strategy encompasses 89 total trades with a 58% win rate, generating an average winning trade of $180 and average losing trade of $95. The maximum drawdown reached 12% during testing, with available capital of $10,000. The strategy employs fixed stop losses at 25 pips and take profit targets at 45 pips, providing clear risk-reward parameters.
Implementation begins with loading the calculator on the EURUSD 1-hour chart for appropriate timeframe alignment. Parameter configuration includes win rate at 58, average win at 180, and average loss at 95. Total historical trades input receives 89, with account size set to 10000. The stop loss function is enabled with distance set to 25 pips, reflecting the fixed exit strategy. Quarter Kelly mode provides conservative positioning due to the smaller sample size compared to the previous example.
Results demonstrate the impact of smaller sample sizes on Kelly calculations. The risk-reward ratio calculates to 1.89, while the Kelly fraction reaches approximately 32% before adjustments. Sample confidence achieves 89%, providing moderate statistical confidence given the 89 trades. The recommended position settles at approximately 7% after Quarter Kelly application and Bayesian shrinkage adjustment for the smaller sample. Position value amounts to approximately $700, translating to 0.07 standard lots. Risk per trade reaches approximately $175, calculated as 25 pips multiplied by lot size and pip value, with expected value per trade at approximately $49. This conservative position sizing reflects the smaller sample size, with position sizes expected to increase as trade count surpasses 100 and statistical confidence improves.
Example 3: ES1! Futures Systematic Strategy
Systematic futures trading presents unique considerations for Kelly criterion application, as demonstrated by an E-mini S&P 500 futures strategy encompassing 234 total trades. This systematic approach achieved a 45% win rate with an average winning trade of $1,850 and average losing trade of $720. The maximum drawdown reached 18% during the testing period, with available capital of $50,000. The strategy employs 15-tick stop losses with contract specifications of $50 per tick, providing precise risk control mechanisms.
Implementation involves loading the calculator on the ES1! 15-minute chart to align with the systematic trading timeframe. Parameter configuration includes win rate at 45, average win at 1850, and average loss at 720. Total historical trades receives 234, providing robust statistical foundation, with account size set to 50000. The stop loss function is enabled with distance set to 15 ticks, reflecting the systematic exit methodology. Half Kelly mode balances growth potential with appropriate risk management for futures trading.
Results illustrate how favorable risk-reward ratios can support meaningful position sizing despite lower win rates. The risk-reward ratio calculates to 2.57, while the Kelly fraction reaches approximately 16%, lower than previous examples due to the sub-50% win rate. Sample confidence achieves 100% given the 234 trades providing high statistical confidence. The recommended position settles at approximately 8% after Half Kelly adjustment. Estimated margin per contract amounts to approximately $2,500, resulting in a single contract allocation. Position value reaches approximately $2,500, with risk per trade at $750, calculated as 15 ticks multiplied by $50 per tick. Expected value per trade amounts to approximately $508. Despite the lower win rate, the favorable risk-reward ratio supports meaningful position sizing, with single contract allocation reflecting appropriate leverage management for futures trading.
Example 4: MES1! Micro-Futures for Smaller Accounts
Micro-futures contracts provide enhanced accessibility for smaller trading accounts while maintaining identical strategy characteristics. Using the same systematic strategy statistics from the previous example but with available capital of $15,000 and micro-futures specifications of $5 per tick with reduced margin requirements, the implementation demonstrates improved position sizing granularity.
Kelly calculations remain identical to the full-sized contract example, maintaining the same risk-reward dynamics and statistical foundations. However, estimated margin per contract reduces to approximately $250 for micro-contracts, enabling allocation of 4-5 micro-contracts. Position value reaches approximately $1,200, while risk per trade calculates to $75, derived from 15 ticks multiplied by $5 per tick. This granularity advantage provides better position size precision for smaller accounts, enabling more accurate Kelly implementation without requiring large capital commitments.
Example 5: Bitcoin Swing Trading
Cryptocurrency markets present unique challenges requiring modified Kelly application approaches. A Bitcoin swing trading strategy on BTCUSD encompasses 67 total trades with a 71% win rate, generating average winning trades of $3,200 and average losing trades of $1,400. Maximum drawdown reached 28% during testing, with available capital of $30,000. The strategy employs technical analysis for exits without fixed stop losses, relying on price action and momentum indicators.
Implementation requires conservative approaches due to cryptocurrency volatility characteristics. Quarter Kelly mode is recommended despite the high win rate to account for crypto market unpredictability. Expected position sizing remains reduced due to the limited sample size of 67 trades, requiring additional caution until statistical confidence improves. Regular parameter updates are strongly recommended due to cryptocurrency market evolution and changing volatility patterns that can significantly impact strategy performance characteristics.
Advanced Usage Scenarios
Portfolio position sizing requires sophisticated consideration when running multiple strategies simultaneously. Each strategy should have its Kelly fraction calculated independently to maintain mathematical integrity. However, correlation adjustments become necessary when strategies exhibit related performance patterns. Moderately correlated strategies should receive individual position size reductions of 10-20% to account for overlapping risk exposure. Aggregate portfolio risk monitoring ensures total exposure remains within acceptable limits across all active strategies. Professional practitioners often consider using lower fractional Kelly approaches, such as Quarter Kelly, when running multiple strategies simultaneously to provide additional safety margins.
Parameter sensitivity analysis forms a critical component of professional Kelly implementation. Regular validation procedures should include monthly parameter updates using rolling 100-trade windows to capture evolving market conditions while maintaining statistical relevance. Sensitivity testing involves varying win rates by ±5% and average win/loss ratios by ±10% to assess recommendation stability under different parameter assumptions. Out-of-sample validation reserves 20% of historical data for parameter verification, ensuring that optimization doesn't create curve-fitted results. Regime change detection monitors actual performance against expected metrics, triggering parameter reassessment when significant deviations occur.
Risk management integration requires professional overlay considerations beyond pure Kelly calculations. Daily loss limits should cease trading when daily losses exceed twice the calculated risk per trade, preventing emotional decision-making during adverse periods. Maximum position limits should never exceed 25% of account value in any single position regardless of Kelly recommendations, maintaining diversification principles. Correlation monitoring reduces position sizes when holding multiple correlated positions that move together during market stress. Volatility adjustments consider reducing position sizes during periods of elevated VIX above 25 for equity strategies, adapting to changing market conditions.
Troubleshooting and Optimization
Professional implementation often encounters specific challenges requiring systematic troubleshooting approaches. Zero position size displays typically result from insufficient capital for minimum position sizes, negative expected values, or extremely conservative Kelly calculations. Solutions include increasing account size, verifying strategy statistics for accuracy, considering Quarter Kelly mode for conservative approaches, or reassessing overall strategy viability when fundamental issues exist.
Extremely high Kelly fractions exceeding 50% usually indicate underlying problems with parameter estimation. Common causes include unrealistic win rates, inflated risk-reward ratios, or curve-fitted backtest results that don't reflect genuine trading conditions. Solutions require verifying backtest methodology, including all transaction costs in calculations, testing strategies on out-of-sample data, and using conservative fractional Kelly approaches until parameter reliability improves.
Low sample confidence below 50% reflects insufficient historical trades for reliable parameter estimation. This situation demands gathering additional trading data, using Quarter Kelly approaches until reaching 100 or more trades, applying extra conservatism in position sizing, and considering paper trading to build statistical foundations without capital risk.
Inconsistent results across similar strategies often stem from parameter estimation differences, market regime changes, or strategy degradation over time. Professional solutions include standardizing backtest methodology across all strategies, updating parameters regularly to reflect current conditions, and monitoring live performance against expectations to identify deteriorating strategies.
Position sizes that appear inappropriately large or small require careful validation against traditional risk management principles. Professional standards recommend never risking more than 2-3% per trade regardless of Kelly calculations. Calibration should begin with Quarter Kelly approaches, gradually increasing as comfort and confidence develop. Most institutional traders utilize 25-50% of full Kelly recommendations to balance growth with prudent risk management.
Market condition adjustments require dynamic approaches to Kelly implementation. Trending markets may support full Kelly recommendations when directional momentum provides favorable conditions. Ranging or volatile markets typically warrant reducing to Half or Quarter Kelly to account for increased uncertainty. High correlation periods demand reducing individual position sizes when multiple positions move together, concentrating risk exposure. News and event periods often justify temporary position size reductions during high-impact releases that can create unpredictable market movements.
Performance monitoring requires systematic protocols to ensure Kelly implementation remains effective over time. Weekly reviews should compare actual versus expected win rates and average win/loss ratios to identify parameter drift or strategy degradation. Position size efficiency and execution quality monitoring ensures that calculated recommendations translate effectively into actual trading results. Tracking correlation between calculated and realized risk helps identify discrepancies between theoretical and practical risk exposure.
Monthly calibration provides more comprehensive parameter assessment using the most recent 100 trades to maintain statistical relevance while capturing current market conditions. Kelly mode appropriateness requires reassessment based on recent market volatility and performance characteristics, potentially shifting between Full, Half, and Quarter Kelly approaches as conditions change. Transaction cost evaluation ensures that commission structures, spreads, and slippage estimates remain accurate and current.
Quarterly strategic reviews encompass comprehensive strategy performance analysis comparing long-term results against expectations and identifying trends in effectiveness. Market regime assessment evaluates parameter stability across different market conditions, determining whether strategy characteristics remain consistent or require fundamental adjustments. Strategic modifications to position sizing methodology may become necessary as markets evolve or trading approaches mature, ensuring that Kelly implementation continues supporting optimal capital allocation objectives.
Professional Applications
This calculator serves diverse professional applications across the financial industry. Quantitative hedge funds utilize the implementation for systematic position sizing within algorithmic trading frameworks, where mathematical precision and consistent application prove essential for institutional capital management. Professional discretionary traders benefit from optimized position management that removes emotional bias while maintaining flexibility for market-specific adjustments. Portfolio managers employ the calculator for developing risk-adjusted allocation strategies that enhance returns while maintaining prudent risk controls across diverse asset classes and investment strategies.
Individual traders seeking mathematical optimization of capital allocation find the calculator provides institutional-grade methodology previously available only to professional money managers. The Kelly Criterion establishes theoretical foundation for optimal capital allocation across both single strategies and multiple trading systems, offering significant advantages over arbitrary position sizing methods that rely on intuition or fixed percentage approaches. Professional implementation ensures consistent application of mathematically sound principles while adapting to changing market conditions and strategy performance characteristics.
Conclusion
The Kelly Criterion represents one of the few mathematically optimal solutions to fundamental investment problems. When properly understood and carefully implemented, it provides significant competitive advantage in financial markets. This calculator implements modern refinements to Kelly's original formula while maintaining accessibility for practical trading applications.
Success with Kelly requires ongoing learning, systematic application, and continuous refinement based on market feedback and evolving research. Users who master Kelly principles and implement them systematically can expect superior risk-adjusted returns and more consistent capital growth over extended periods.
The extensive academic literature provides rich resources for deeper study, while practical experience builds the intuition necessary for effective implementation. Regular parameter updates, conservative approaches with limited data, and disciplined adherence to calculated recommendations are essential for optimal results.
References
Archer, M. D. (2010). Getting Started in Currency Trading: Winning in Today's Forex Market (3rd ed.). John Wiley & Sons.
Baker, R. D., & McHale, I. G. (2012). An empirical Bayes approach to optimising betting strategies. Journal of the Royal Statistical Society: Series D (The Statistician), 61(1), 75-92.
Breiman, L. (1961). Optimal gambling systems for favorable games. In J. Neyman (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability (pp. 65-78). University of California Press.
Brown, D. B. (1976). Optimal portfolio growth: Logarithmic utility and the Kelly criterion. In W. T. Ziemba & R. G. Vickson (Eds.), Stochastic Optimization Models in Finance (pp. 1-23). Academic Press.
Browne, S., & Whitt, W. (1996). Portfolio choice and the Bayesian Kelly criterion. Advances in Applied Probability, 28(4), 1145-1176.
Estrada, J. (2008). Geometric mean maximization: An overlooked portfolio approach? The Journal of Investing, 17(4), 134-147.
Hakansson, N. H., & Ziemba, W. T. (1995). Capital growth theory. In R. A. Jarrow, V. Maksimovic, & W. T. Ziemba (Eds.), Handbooks in Operations Research and Management Science (Vol. 9, pp. 65-86). Elsevier.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Kaufman, P. J. (2013). Trading Systems and Methods (5th ed.). John Wiley & Sons.
Kelly Jr, J. L. (1956). A new interpretation of information rate. Bell System Technical Journal, 35(4), 917-926.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton University Press.
MacLean, L. C., Sanegre, E. O., Zhao, Y., & Ziemba, W. T. (2004). Capital growth with security. Journal of Economic Dynamics and Control, 28(4), 937-954.
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Michaud, R. O. (1989). The Markowitz optimization enigma: Is 'optimized' optimal? Financial Analysts Journal, 45(1), 31-42.
Pabrai, M. (2007). The Dhandho Investor: The Low-Risk Value Method to High Returns. John Wiley & Sons.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.
Tharp, V. K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill.
Thorp, E. O. (2006). The Kelly criterion in blackjack sports betting, and the stock market. In L. C. MacLean, E. O. Thorp, & W. T. Ziemba (Eds.), The Kelly Capital Growth Investment Criterion: Theory and Practice (pp. 789-832). World Scientific.
Van Tharp, K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill Education.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Vince, R., & Zhu, H. (2015). Optimal betting under parameter uncertainty. Journal of Statistical Planning and Inference, 161, 19-31.
Ziemba, W. T. (2003). The Stochastic Programming Approach to Asset, Liability, and Wealth Management. The Research Foundation of AIMR.
Further Reading
For comprehensive understanding of Kelly Criterion applications and advanced implementations:
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Thorp, E. O. (2017). A Man for All Markets: From Las Vegas to Wall Street. Random House.
Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory (2nd ed.). John Wiley & Sons.
Ziemba, W. T., & Vickson, R. G. (Eds.). (2006). Stochastic Optimization Models in Finance. World Scientific.
Binance Spot vs Perpetual Price index by BIGTAKER📌 Overview
This indicator calculates the premium (%) between Binance Perpetual Futures and Spot prices in real time and visualizes it as a column-style chart.
It automatically detects numeric prefixes in futures symbols—such as `1000PEPE`, `1MFLUX`, etc.—and applies the appropriate scaling factor to ensure accurate 1:1 price comparisons with corresponding spot pairs, without requiring manual configuration.
Rather than simply showing raw price differences, this tool highlights potential imbalances in supply and demand, helping to identify phases of market overheating or panic selling.
🔧 Component Breakdown
1. ✅ Auto Symbol Mapping & Prefix Scaling
Automatically identifies and processes common numeric prefixes (`1000`, `1M`, etc.) used in Binance perpetual futures symbols.
Example:
`1000PEPEUSDT.P` → Spot symbol: `PEPEUSDT`, Scaling factor: `1000`
This ensures precise alignment between futures and spot prices by adjusting the scale appropriately.
2. 📈 Premium Calculation Logic
Formula:
(Scaled Futures Price − Spot Price) / Spot Price × 100
Interpretation:
* Positive (+) → Futures are priced higher than spot: indicates possible long-side euphoria
* Negative (−) → Futures are priced lower than spot: indicates possible panic selling or oversold conditions
* Zero → Equilibrium between futures and spot pricing
3. 🎨 Visualization Style
* Rendered as column plots (bar chart) on each candle
* Color-coded based on premium polarity:
* 🟩 Positive premium: Light green (`#52ff7d`)
* 🟥 Negative premium: Light red (`#f56464`)
* ⬜ Neutral / NA: Gray
* A dashed horizontal line at 0% is included to indicate the neutral zone for quick visual reference
💡 Strategic Use Cases
| Market Behavior | Strategy / Interpretation |
| ----------------------------------------- | ------------------------------------------------------------------------ |
| 📈 Premium surging | Strong futures demand → Overheated longs (short setup) |
| 📉 Premium dropping | Aggressive selling in futures → Oversold signal (long setup) |
| 🔄 Near-zero premium | Balanced market → Wait and observe or reassess |
| 🧩 Combined with funding rate or OI delta | Enables multi-factor confirmation for short-term or mid-term signals |
🧠 Technical Advantages
* Fully automated scaling for prefixes like `1000`, `1M`, etc.
* Built-in error handling for inactive or missing symbols (`ignore_invalid_symbol=true`)
* Broad compatibility with Binance USDT Spot & Perpetual Futures markets
🔍 Target Use Cases & Examples
Compatible symbols:
`1000PEPEUSDT.P`, `DOGEUSDT.P`, `1MFLUXUSDT.P`, `ETHUSDT.P`, and most other Binance USDT-margined perpetual futures
Works seamlessly with:
* Binance Spot Market
* Binance Perpetual Futures Market
Dskyz (DAFE) Adaptive Regime - Quant Machine ProDskyz (DAFE) Adaptive Regime - Quant Machine Pro:
Buckle up for the Dskyz (DAFE) Adaptive Regime - Quant Machine Pro, is a strategy that’s your ultimate edge for conquering futures markets like ES, MES, NQ, and MNQ. This isn’t just another script—it’s a quant-grade powerhouse, crafted with precision to adapt to market regimes, deliver multi-factor signals, and protect your capital with futures-tuned risk management. With its shimmering DAFE visuals, dual dashboards, and glowing watermark, it turns your charts into a cyberpunk command center, making trading as thrilling as it is profitable.
Unlike generic scripts clogging up the space, the Adaptive Regime is a DAFE original, built from the ground up to tackle the chaos of futures trading. It identifies market regimes (Trending, Range, Volatile, Quiet) using ADX, Bollinger Bands, and HTF indicators, then fires trades based on a weighted scoring system that blends candlestick patterns, RSI, MACD, and more. Add in dynamic stops, trailing exits, and a 5% drawdown circuit breaker, and you’ve got a system that’s as safe as it is aggressive. Whether you’re a newbie or a prop desk pro, this strat’s your ticket to outsmarting the markets. Let’s break down every detail and see why it’s a must-have.
Why Traders Need This Strategy
Futures markets are a gauntlet—fast moves, volatility spikes (like the April 28, 2025 NQ 1k-point drop), and institutional traps that punish the unprepared. Meanwhile, platforms are flooded with low-effort scripts that recycle old ideas with zero innovation. The Adaptive Regime stands tall, offering:
Adaptive Intelligence: Detects market regimes (Trending, Range, Volatile, Quiet) to optimize signals, unlike one-size-fits-all scripts.
Multi-Factor Precision: Combines candlestick patterns, MA trends, RSI, MACD, volume, and HTF confirmation for high-probability trades.
Futures-Optimized Risk: Calculates position sizes based on $ risk (default: $300), with ATR or fixed stops/TPs tailored for ES/MES.
Bulletproof Safety: 5% daily drawdown circuit breaker and trailing stops keep your account intact, even in chaos.
DAFE Visual Mastery: Pulsing Bollinger Band fills, dynamic SL/TP lines, and dual dashboards (metrics + position) make signals crystal-clear and charts a work of art.
Original Craftsmanship: A DAFE creation, built with community passion, not a rehashed clone of generic code.
Traders need this because it’s a complete, adaptive system that blends quant smarts, user-friendly design, and DAFE flair. It’s your edge to trade with confidence, cut through market noise, and leave the copycats in the dust.
Strategy Components
1. Market Regime Detection
The strategy’s brain is its ability to classify market conditions into five regimes, ensuring signals match the environment.
How It Works:
Trending (Regime 1): ADX > 20, fast/slow EMA spread > 0.3x ATR, HTF RSI > 50 or MACD bullish (htf_trend_bull/bear).
Range (Regime 2): ADX < 25, price range < 3% of close, no HTF trend.
Volatile (Regime 3): BB width > 1.5x avg, ATR > 1.2x avg, HTF RSI overbought/oversold.
Quiet (Regime 4): BB width < 0.8x avg, ATR < 0.9x avg.
Other (Regime 5): Default for unclear conditions.
Indicators: ADX (14), BB width (20), ATR (14, 50-bar SMA), HTF RSI (14, daily default), HTF MACD (12,26,9).
Why It’s Brilliant:
Regime detection adapts signals to market context, boosting win rates in trending or volatile conditions.
HTF RSI/MACD add a big-picture filter, rare in basic scripts.
Visualized via gradient background (green for Trending, orange for Range, red for Volatile, gray for Quiet, navy for Other).
2. Multi-Factor Signal Scoring
Entries are driven by a weighted scoring system that combines candlestick patterns, trend, momentum, and volume for robust signals.
Candlestick Patterns:
Bullish: Engulfing (0.5), hammer (0.4 in Range, 0.2 else), morning star (0.2), piercing (0.2), double bottom (0.3 in Volatile, 0.15 else). Must be near support (low ≤ 1.01x 20-bar low) with volume spike (>1.5x 20-bar avg).
Bearish: Engulfing (0.5), shooting star (0.4 in Range, 0.2 else), evening star (0.2), dark cloud (0.2), double top (0.3 in Volatile, 0.15 else). Must be near resistance (high ≥ 0.99x 20-bar high) with volume spike.
Logic: Patterns are weighted higher in specific regimes (e.g., hammer in Range, double bottom in Volatile).
Additional Factors:
Trend: Fast EMA (20) > slow EMA (50) + 0.5x ATR (trend_bull, +0.2); opposite for trend_bear.
RSI: RSI (14) < 30 (rsi_bull, +0.15); > 70 (rsi_bear, +0.15).
MACD: MACD line > signal (12,26,9, macd_bull, +0.15); opposite for macd_bear.
Volume: ATR > 1.2x 50-bar avg (vol_expansion, +0.1).
HTF Confirmation: HTF RSI < 70 and MACD bullish (htf_bull_confirm, +0.2); RSI > 30 and MACD bearish (htf_bear_confirm, +0.2).
Scoring:
bull_score = sum of bullish factors; bear_score = sum of bearish. Entry requires score ≥ 1.0.
Example: Bullish engulfing (0.5) + trend_bull (0.2) + rsi_bull (0.15) + htf_bull_confirm (0.2) = 1.05, triggers long.
Why It’s Brilliant:
Multi-factor scoring ensures signals are confirmed by multiple market dynamics, reducing false positives.
Regime-specific weights make patterns more relevant (e.g., hammers shine in Range markets).
HTF confirmation aligns with the big picture, a quant edge over simplistic scripts.
3. Futures-Tuned Risk Management
The risk system is built for futures, calculating position sizes based on $ risk and offering flexible stops/TPs.
Position Sizing:
Logic: Risk per trade (default: $300) ÷ (stop distance in points * point value) = contracts, capped at max_contracts (default: 5). Point value = tick value (e.g., $12.5 for ES) * ticks per point (4) * contract multiplier (1 for ES, 0.1 for MES).
Example: $300 risk, 8-point stop, ES ($50/point) → 0.75 contracts, rounded to 1.
Impact: Precise sizing prevents over-leverage, critical for micro contracts like MES.
Stops and Take-Profits:
Fixed: Default stop = 8 points, TP = 16 points (2:1 reward/risk).
ATR-Based: Stop = 1.5x ATR (default), TP = 3x ATR, enabled via use_atr_for_stops.
Logic: Stops set at swing low/high ± stop distance; TPs at 2x stop distance from entry.
Impact: ATR stops adapt to volatility, while fixed stops suit stable markets.
Trailing Stops:
Logic: Activates at 50% of TP distance. Trails at close ± 1.5x ATR (atr_multiplier). Longs: max(trail_stop_long, close - ATR * 1.5); shorts: min(trail_stop_short, close + ATR * 1.5).
Impact: Locks in profits during trends, a game-changer in volatile sessions.
Circuit Breaker:
Logic: Pauses trading if daily drawdown > 5% (daily_drawdown = (max_equity - equity) / max_equity).
Impact: Protects capital during black swan events (e.g., April 27, 2025 ES slippage).
Why It’s Brilliant:
Futures-specific inputs (tick value, multiplier) make it plug-and-play for ES/MES.
Trailing stops and circuit breaker add pro-level safety, rare in off-the-shelf scripts.
Flexible stops (ATR or fixed) suit different trading styles.
4. Trade Entry and Exit Logic
Entries and exits are precise, driven by bull_score/bear_score and protected by drawdown checks.
Entry Conditions:
Long: bull_score ≥ 1.0, no position (position_size <= 0), drawdown < 5% (not pause_trading). Calculates contracts, sets stop at swing low - stop points, TP at 2x stop distance.
Short: bear_score ≥ 1.0, position_size >= 0, drawdown < 5%. Stop at swing high + stop points, TP at 2x stop distance.
Logic: Tracks entry_regime for PNL arrays. Closes opposite positions before entering.
Exit Conditions:
Stop-Loss/Take-Profit: Hits stop or TP (strategy.exit).
Trailing Stop: Activates at 50% TP, trails by ATR * 1.5.
Emergency Exit: Closes if price breaches stop (close < long_stop_price or close > short_stop_price).
Reset: Clears stop/TP prices when flat (position_size = 0).
Why It’s Brilliant:
Score-based entries ensure multi-factor confirmation, filtering out weak signals.
Trailing stops maximize profits in trends, unlike static exits in basic scripts.
Emergency exits add an extra safety layer, critical for futures volatility.
5. DAFE Visuals
The visuals are pure DAFE magic, blending function with cyberpunk flair to make signals intuitive and charts stunning.
Shimmering Bollinger Band Fill:
Display: BB basis (20, white), upper/lower (green/red, 45% transparent). Fill pulses (30–50 alpha) by regime, with glow (60–95 alpha) near bands (close ≥ 0.995x upper or ≤ 1.005x lower).
Purpose: Highlights volatility and key levels with a futuristic glow.
Visuals make complex regimes and signals instantly clear, even for newbies.
Pulsing effects and regime-specific colors add a DAFE signature, setting it apart from generic scripts.
BB glow emphasizes tradeable levels, enhancing decision-making.
Chart Background (Regime Heatmap):
Green — Trending Market: Strong, sustained price movement in one direction. The market is in a trend phase—momentum follows through.
Orange — Range-Bound: Market is consolidating or moving sideways, with no clear up/down trend. Great for mean reversion setups.
Red — Volatile Regime: High volatility, heightened risk, and larger/faster price swings—trade with caution.
Gray — Quiet/Low Volatility: Market is calm and inactive, with small moves—often poor conditions for most strategies.
Navy — Other/Neutral: Regime is uncertain or mixed; signals may be less reliable.
Bollinger Bands Glow (Dynamic Fill):
Neon Red Glow — Warning!: Price is near or breaking above the upper band; momentum is overstretched, watch for overbought conditions or reversals.
Bright Green Glow — Opportunity!: Price is near or breaking below the lower band; market could be oversold, prime for bounce or reversal.
Trend Green Fill — Trending Regime: Fills between bands with green when the market is trending, showing clear momentum.
Gold/Yellow Fill — Range Regime: Fills with gold/aqua in range conditions, showing the market is sideways/oscillating.
Magenta/Red Fill — Volatility Spike: Fills with vivid magenta/red during highly volatile regimes.
Blue Fill — Neutral/Quiet: A soft blue glow for other or uncertain market states.
Moving Averages:
Display: Blue fast EMA (20), red slow EMA (50), 2px.
Purpose: Shows trend direction, with trend_dir requiring ATR-scaled spread.
Dynamic SL/TP Lines:
Display: Pulsing colors (red SL, green TP for Trending; yellow/orange for Range, etc.), 3px, with pulse_alpha for shimmer.
Purpose: Tracks stops/TPs in real-time, color-coded by regime.
6. Dual Dashboards
Two dashboards deliver real-time insights, making the strat a quant command center.
Bottom-Left Metrics Dashboard (2x13):
Metrics: Mode (Active/Paused), trend (Bullish/Bearish/Neutral), ATR, ATR avg, volume spike (YES/NO), RSI (value + Oversold/Overbought/Neutral), HTF RSI, HTF trend, last signal (Buy/Sell/None), regime, bull score.
Display: Black (29% transparent), purple title, color-coded (green for bullish, red for bearish).
Purpose: Consolidates market context and signal strength.
Top-Right Position Dashboard (2x7):
Metrics: Regime, position side (Long/Short/None), position PNL ($), SL, TP, daily PNL ($).
Display: Black (29% transparent), purple title, color-coded (lime for Long, red for Short).
Purpose: Tracks live trades and profitability.
Why It’s Brilliant:
Dual dashboards cover market context and trade status, a rare feature.
Color-coding and concise metrics guide beginners (e.g., green “Buy” = go).
Real-time PNL and SL/TP visibility empower disciplined trading.
7. Performance Tracking
Logic: Arrays (regime_pnl_long/short, regime_win/loss_long/short) track PNL and win/loss by regime (1–5). Updated on trade close (barstate.isconfirmed).
Purpose: Prepares for future adaptive thresholds (e.g., adjust bull_score min based on regime performance).
Why It’s Brilliant: Lays the groundwork for self-optimizing logic, a quant edge over static scripts.
Key Features
Regime-Adaptive: Optimizes signals for Trending, Range, Volatile, Quiet markets.
Futures-Optimized: Precise sizing for ES/MES with tick-based risk inputs.
Multi-Factor Signals: Candlestick patterns, RSI, MACD, and HTF confirmation for robust entries.
Dynamic Exits: ATR/fixed stops, 2:1 TPs, and trailing stops maximize profits.
Safe and Smart: 5% drawdown breaker and emergency exits protect capital.
DAFE Visuals: Shimmering BB fill, pulsing SL/TP, and dual dashboards.
Backtest-Ready: Fixed qty and tick calc for accurate historical testing.
How to Use
Add to Chart: Load on a 5min ES/MES chart in TradingView.
Configure Inputs: Set instrument (ES/MES), tick value ($12.5/$1.25), multiplier (1/0.1), risk ($300 default). Enable ATR stops for volatility.
Monitor Dashboards: Bottom-left for regime/signals, top-right for position/PNL.
Backtest: Run in strategy tester to compare regimes.
Live Trade: Connect to Tradovate or similar. Watch for slippage (e.g., April 27, 2025 ES issues).
Replay Test: Try April 28, 2025 NQ drop to see regime shifts and stops.
Disclaimer
Trading futures involves significant risk of loss and is not suitable for all investors. Past performance does not guarantee future results. Backtest results may differ from live trading due to slippage, fees, or market conditions. Use this strategy at your own risk, and consult a financial advisor before trading. Dskyz (DAFE) Trading Systems is not responsible for any losses incurred.
Backtesting:
Frame: 2023-09-20 - 2025-04-29
Slippage: 3
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Final Notes
The Dskyz (DAFE) Adaptive Regime - Quant Machine Pro is more than a strategy—it’s a revolution. Crafted with DAFE’s signature precision, it rises above generic scripts with adaptive regimes, quant-grade signals, and visuals that make trading a thrill. Whether you’re scalping MES or swinging ES, this system empowers you to navigate markets with confidence and style. Join the DAFE crew, light up your charts, and let’s dominate the futures game!
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Bitcoin cme gap indicators, BINANCE vs CME exchanges premium gap
# CME BTC Premium Indicator Documentation CME:BTC1!
## 1. Overview
Indicator Name: CME BTC Premium
Platform: TradingView (Pine Script v6)
Type: Premium / Gap Analysis
Purpose:
* Visualize the CME BTC futures premium/discount relative to Binance BTCUSDT spot price.
* Detect gap-up or gap-down events on the daily chart.
* Assess short-term market sentiment and potential volatility through price discrepancies.
## 2. Key Features
1. CME Premium Calculation
* Formula:
CME Premium(%) = ((CME Price - Binance Price) / Binance Price) X 100
* Positive premium: CME futures are higher than spot → Color: Blue
* Negative premium: CME futures are lower than spot → Color: Purple
2. Premium Visualization Options
* `Column` (default)
* `Line`
3. Daily Gap Detection (Daily Chart Only)
* Gap Up: CME open > previous high × 1.0001 (≥ 0.01%)
* Gap Down: CME open < previous low × 0.9999 (≤ 0.01%)
* Visualization:
* Bar Color:
* Gap Up → Yellow (semi-transparent)
* Gap Down → Blue (semi-transparent)
* Background Color:
* Gap Up → Yellow (semi-transparent)
* Gap Down → Blue (semi-transparent)
4. Label Display
* If `Show CME Label` is enabled, the last bar displays a premium percentage label.
* Label color matches premium color; text color: Black.
* Style: `style_label_upper_left`, Size: `small`.
## 3. User Inputs
| Option Name | Description | Type / Default |
| -------------- | ------------------------- | --------------------------------------- |
| Show CME Label | Display CME premium label | Boolean / true |
| CME Plot Type | CME premium chart style | String / Column (Options: Column, Line) |
## 4. Data Sources
| Data Item | Symbol | Description |
| ------------- | ---------------- | ----------------------------- |
| Binance Price | BINANCE\:BTCUSDT | Spot BTC price |
| CME Price | CME\:BTC1! | CME BTC futures closing price |
| CME Open | CME\:BTC1! | CME BTC futures open price |
| CME Low | CME\:BTC1! | CME BTC futures low price |
| CME High | CME\:BTC1! | CME BTC futures high price |
## 5. Chart Display
1. Premium Column/Line
* Displays the CME premium percentage in real-time.
* Color: Premium ≥ 0 → Blue, Premium < 0 → Purple
2. Zero Line
* Indicates CME futures are at parity with spot for quick visual reference.
3. Gap Highlight
* Applied only on daily charts.
* Gap-up or gap-down is highlighted using bar and background colors.
4. Label
* Shows the latest CME premium percentage for quick monitoring.
## 6. Use Cases
* Analyze spot-futures premium to gauge CME market sentiment.
* Identify short-term volatility and potential trend reversals through daily gaps.
* Combine premium and gap analysis to support altcoin trend analysis and position strategy.
## 7. Limitations
* This indicator does not provide investment advice or trading recommendations; it is for informational purposes only.
* Data delays, API restrictions, or exchange differences may result in calculation discrepancies.
* Gap detection is meaningful only on daily charts; other timeframes may not provide valid signals.
Spread AnalysisSpread Analysis - Futures vs Spot Price Analysis
Advanced spread analysis tool that compares futures/perp prices with spot prices across multiple exchanges, providing insights into market sentiment and potential trading opportunities.
Multi-Asset Support: Automatically detects and analyzes crypto perpetual vs spot spreads, index futures vs cash indices (ES/SPX, NQ/NDX, YM/DJI), and commodity futures vs spot prices (GC/GOLD, CL/USOIL)
Multi-Exchange Aggregation: For crypto, aggregates prices from Binance, BitMEX, Kraken, Bybit, OKX, and Coinbase to calculate mean perp and spot prices
Z-Score Based Alerts: Uses statistical Z-score analysis to identify extreme spread conditions that may signal potential reversals or continuation patterns
Visual Histogram Display: Shows spread differences as colored columns - green for futures premium, red for futures discount
Flexible Calculation Methods: Supports absolute price differences, percentage spreads, or basis point calculations
Trading Applications: Identify market sentiment divergence, spot potential reversal opportunities, and confirm trend strength
Risk Management: Use extreme Z-scores to identify overvalued conditions and potential mean reversion setups
Market Analysis: Understand the relationship between futures and spot markets across different asset classes
Timing Tool: Spread momentum often precedes price moves, providing early signals for entry/exit decisions
Perfect for traders who want to understand the relationship between futures and spot markets, identify divergences, and spot potential reversal opportunities across crypto, indices, and commodities.
Key Features:
• Automatic asset detection and appropriate spread calculation
• Configurable Z-score alerts for extreme conditions
• Comprehensive tooltips and information guide
• Multiple calculation methods (absolute, percentage, basis points)
• Clean, customizable visual display
Use Cases:
• Crypto traders analyzing perp vs spot relationships
• Futures traders monitoring basis relationships
• Mean reversion strategies using extreme spreads
• Trend confirmation using spread momentum
• Market sentiment analysis across asset classes
Spot - Fut spread v2"Spot - Fut Spread v2"
indicator is designed to track the difference between spot and futures prices on various exchanges. It automatically identifies the corresponding instrument (spot or futures) based on the current symbol and calculates the spread between the prices. This tool is useful for analyzing the delta between spot and futures markets, helping traders assess arbitrage opportunities and market sentiment.
Key Features:
- Automatic detection of spot and futures assets based on the current chart symbol.
- Flexible asset selection: the ability to manually choose the second asset if automatic selection is disabled.
- Spread calculation between futures and spot prices.
- Moving average of the spread for smoothing data and trend analysis.
Flexible visualization:
- Color indication of positive and negative spread.
- Adjustable background transparency.
- Text label displaying the current spread and moving average values.
- Error alerts in case of invalid data.
How the Indicator Works:
- Determines whether the current symbol is a futures contract.
- Based on this, selects the corresponding spot or futures symbol.
- Retrieves price data and calculates the spread between them.
- Displays the spread value and its moving average.
- The chart background color changes based on the spread value (positive or negative).
- In case of an error, the indicator provides an alert with an explanation.
Customization Parameters:
-Exchange selection: the ability to specify a particular exchange from the list.
- Automatic pair selection: enable or disable automatic selection of the second asset.
- Moving average period: user-defined.
- Colors for positive and negative spread values.
- Moving average color.
- Background transparency.
- Background coloring source (based on spread or its moving average).
Application:
The indicator is suitable for traders who analyze the difference between spot and futures prices, look for arbitrage opportunities, and assess the premium or discount of futures relative to the spot market.
[GYTS] FiltersToolkit LibraryFiltersToolkit Library
🌸 Part of GoemonYae Trading System (GYTS) 🌸
🌸 --------- 1. INTRODUCTION --------- 🌸
💮 What Does This Library Contain?
This library is a curated collection of high-performance digital signal processing (DSP) filters and auxiliary functions designed specifically for financial time series analysis. It includes a shortlist of our favourite and best performing filters — each rigorously tested and selected for their responsiveness, minimal lag and robustness in diverse market conditions. These tools form an integral part of the GoemonYae Trading System (GYTS), chosen for their unique characteristics in handling market data.
The library contains two main categories:
1. Smoothing filters (low-pass filters and moving averages) for e.g. denoising, trend following
2. Detrending tools (high-pass and band-pass filters, known as "oscillators") for e.g. mean reversion
This collection is finely tuned for practical trading applications and is therefore not meant to be exhaustive. However, will continue to expand as we discover and validate new filtering techniques. I welcome collaboration and suggestions for novel approaches.
🌸 ——— 2. ADDED VALUE ——— 🌸
💮 Unified syntax and comprehensive documentation
The FiltersToolkit Library brings together a wide array of valuable filters under a unified, intuitive syntax. Each function is thoroughly documented, with clear explanations and academic sources that underline the mathematical rigour behind the methods. This level of documentation not only facilitates integration into trading strategies but also helps underlying the underlying concepts and rationale.
💮 Optimised performance and readability
The code prioritizes computational efficiency while maintaining readability. Key optimizations include:
- Minimizing redundant calculations in recursive filters
- Smart coefficient caching
- Efficient state management
- Vectorized operations where applicable
💮 Enhanced functionality and flexibility
Some filters in this library introduce extended functionality beyond the original publications. For instance, the MESA Adaptive Moving Average (MAMA) and Ehlers’ Combined Bandpass Filter incorporate multiple variations found in the literature, thereby providing traders with flexible tools that can be fine-tuned to different market conditions.
🌸 ——— 3. THE FILTERS ——— 🌸
💮 Hilbert Transform Function
This function implements the Hilbert Transform as utilised by John Ehlers. It converts a real-valued time series into its analytic signal, enabling the extraction of instantaneous phase and frequency information—an essential step in adaptive filtering.
Source: John Ehlers - "Rocket Science for Traders" (2001), "TASC 2001 V. 19:9", "Cybernetic Analysis for Stocks and Futures" (2004)
💮 Homodyne Discriminator
By leveraging the Hilbert Transform, this function computes the dominant cycle period through a Homodyne Discriminator. It extracts the in-phase and quadrature components of the signal, facilitating a robust estimation of the underlying cycle characteristics.
Source: John Ehlers - "Rocket Science for Traders" (2001), "TASC 2001 V. 19:9", "Cybernetic Analysis for Stocks and Futures" (2004)
💮 MESA Adaptive Moving Average (MAMA)
An advanced dual-stage adaptive moving average, this function outputs both the MAMA and its companion FAMA. It combines adaptive alpha computation with elements from Kaufman’s Adaptive Moving Average (KAMA) to provide a responsive and reliable trend indicator.
Source: John Ehlers - "Rocket Science for Traders" (2001), "TASC 2001 V. 19:9", "Cybernetic Analysis for Stocks and Futures" (2004)
💮 BiQuad Filters
A family of second-order recursive filters offering exceptional control over frequency response:
- High-pass filter for detrending
- Low-pass filter for smooth trend following
- Band-pass filter for cycle isolation
The quality factor (Q) parameter allows fine-tuning of the resonance characteristics, making these filters highly adaptable to different market conditions.
Source: Robert Bristow-Johnson's Audio EQ Cookbook, implemented by @The_Peaceful_Lizard
💮 Relative Vigor Index (RVI)
This filter evaluates the strength of a trend by comparing the closing price to the trading range. Operating similarly to a band-pass filter, the RVI provides insights into market momentum and potential reversals.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 Cyber Cycle
The Cyber Cycle filter emphasises market cycles by smoothing out noise and highlighting the dominant cyclical behaviour. It is particularly useful for detecting trend reversals and cyclical patterns in the price data.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 Butterworth High Pass Filter
Inspired by the classical Butterworth design, this filter achieves a maximally flat magnitude response in the passband while effectively removing low-frequency trends. Its design minimises phase distortion, which is vital for accurate signal interpretation.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 2-Pole SuperSmoother
Employing a two-pole design, the SuperSmoother filter reduces high-frequency noise with minimal lag. It is engineered to preserve trend integrity while offering a smooth output even in noisy market conditions.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 3-Pole SuperSmoother
An extension of the 2-pole design, the 3-pole SuperSmoother further attenuates high-frequency noise. Its additional pole delivers enhanced smoothing at the cost of slightly increased lag.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 Adaptive Directional Volatility Moving Average (ADXVma)
This adaptive moving average adjusts its smoothing factor based on directional volatility. By combining true range and directional movement measurements, it remains exceptionally flat during ranging markets and responsive during directional moves.
Source: Various implementations across platforms, unified and optimized
💮 Ehlers Combined Bandpass Filter with Automated Gain Control (AGC)
This sophisticated filter merges a highpass pre-processing stage with a bandpass filter. An integrated Automated Gain Control normalises the output to a consistent range, while offering both regular and truncated recursive formulations to manage lag.
Source: John F. Ehlers – “Truncated Indicators” (2020), “Cycle Analytics for Traders” (2013)
💮 Voss Predictive Filter
A forward-looking filter that predicts future values of a band-limited signal in real time. By utilising multiple time-delayed feedback terms, it provides anticipatory coupling and delivers a short-term predictive signal.
Source: John Ehlers - "A Peek Into The Future" (TASC 2019-08)
💮 Adaptive Autonomous Recursive Moving Average (A2RMA)
This filter dynamically adjusts its smoothing through an adaptive mechanism based on an efficiency ratio and a dynamic threshold. A double application of an adaptive moving average ensures both responsiveness and stability in volatile and ranging markets alike. Very flat response when properly tuned.
Source: @alexgrover (2019)
💮 Ultimate Smoother (2-Pole)
The Ultimate Smoother filter is engineered to achieve near-zero lag in its passband by subtracting a high-pass response from an all-pass response. This creates a filter that maintains signal fidelity at low frequencies while effectively filtering higher frequencies at the expense of slight overshooting.
Source: John Ehlers - TASC 2024-04 "The Ultimate Smoother"
Note: This library is actively maintained and enhanced. Suggestions for additional filters or improvements are welcome through the usual channels. The source code contains a list of tested filters that did not make it into the curated collection.
Commitment of Trader %R StrategyThis Pine Script strategy utilizes the Commitment of Traders (COT) data to inform trading decisions based on the Williams %R indicator. The script operates in TradingView and includes various functionalities that allow users to customize their trading parameters.
Here’s a breakdown of its key components:
COT Data Import:
The script imports the COT library from TradingView to access historical COT data related to different trader groups (commercial hedgers, large traders, and small traders).
User Inputs:
COT data selection mode (e.g., Auto, Root, Base currency).
Whether to include futures, options, or both.
The trader group to analyze.
The lookback period for calculating the Williams %R.
Upper and lower thresholds for triggering trades.
An option to enable or disable a Simple Moving Average (SMA) filter.
Williams %R Calculation: The script calculates the Williams %R value, which is a momentum indicator that measures overbought or oversold levels based on the highest and lowest prices over a specified period.
SMA Filter: An optional SMA filter allows users to limit trades to conditions where the price is above or below the SMA, depending on the configuration.
Trade Logic: The strategy enters long positions when the Williams %R value exceeds the upper threshold and exits when the value falls below it. Conversely, it enters short positions when the Williams %R value is below the lower threshold and exits when the value rises above it.
Visual Elements: The script visually indicates the Williams %R values and thresholds on the chart, with the option to plot the SMA if enabled.
Commitment of Traders (COT) Data
The COT report is a weekly publication by the Commodity Futures Trading Commission (CFTC) that provides a breakdown of open interest positions held by different types of traders in the U.S. futures markets. It is widely used by traders and analysts to gauge market sentiment and potential price movements.
Data Collection: The COT data is collected from futures commission merchants and is published every Friday, reflecting positions as of the previous Tuesday. The report categorizes traders into three main groups:
Commercial Traders: These are typically hedgers (like producers and processors) who use futures to mitigate risk.
Non-Commercial Traders: Often referred to as speculators, these traders do not have a commercial interest in the underlying commodity but seek to profit from price changes.
Non-reportable Positions: Small traders who do not meet the reporting threshold set by the CFTC.
Interpretation:
Market Sentiment: By analyzing the positions of different trader groups, market participants can gauge sentiment. For instance, if commercial traders are heavily short, it may suggest they expect prices to decline.
Extreme Positions: Some traders look for extreme positions among non-commercial traders as potential reversal signals. For example, if speculators are overwhelmingly long, it might indicate an overbought condition.
Statistical Insights: COT data is often used in conjunction with technical analysis to inform trading decisions. Studies have shown that analyzing COT data can provide valuable insights into future price movements (Lund, 2018; Hurst et al., 2017).
Scientific References
Lund, J. (2018). Understanding the COT Report: An Analysis of Speculative Trading Strategies.
Journal of Derivatives and Hedge Funds, 24(1), 41-52. DOI:10.1057/s41260-018-00107-3
Hurst, B., O'Neill, R., & Roulston, M. (2017). The Impact of COT Reports on Futures Market Prices: An Empirical Analysis. Journal of Futures Markets, 37(8), 763-785.
DOI:10.1002/fut.21849
Commodity Futures Trading Commission (CFTC). (2024). Commitment of Traders. Retrieved from CFTC Official Website.
Bitcoin CME-Spot Z-Spread - Strategy [presentTrading]This time is a swing trading strategy! It measures the sentiment of the Bitcoin market through the spread of CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. By applying Bollinger Bands to the spread, the strategy seeks to capture mean-reversion opportunities when prices deviate significantly from their historical norms
█ Introduction and How it is Different
The Bitcoin CME-Spot Bollinger Bands Strategy is designed to capture mean-reversion opportunities by exploiting the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. The strategy uses Bollinger Bands to detect when the spread between these two correlated assets has deviated significantly from its historical norm, signaling potential overbought or oversold conditions.
What sets this strategy apart is its focus on spread trading between futures and spot markets rather than price-based indicators. By applying Bollinger Bands to the spread rather than individual prices, the strategy identifies price inefficiencies across markets, allowing traders to take advantage of the natural reversion to the mean that often occurs in these correlated assets.
BTCUSD 8hr Performance
█ Strategy, How It Works: Detailed Explanation
The strategy relies on Bollinger Bands to assess the volatility and relative deviation of the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. Bollinger Bands consist of a moving average and two standard deviation bands, which help measure how much the spread deviates from its historical mean.
🔶 Spread Calculation:
The spread is calculated by subtracting the Bitfinex spot price from the CME Bitcoin futures price:
Spread = CME Price - Bitfinex Price
This spread represents the difference between the futures and spot markets, which may widen or narrow based on supply and demand dynamics in each market. By analyzing the spread, the strategy can detect when prices are too far apart (potentially overbought or oversold), indicating a trading opportunity.
🔶 Bollinger Bands Calculation:
The Bollinger Bands for the spread are calculated using a simple moving average (SMA) and the standard deviation of the spread over a defined period.
1. Moving Average (SMA):
The simple moving average of the spread (mu_S) over a specified period P is calculated as:
mu_S = (1/P) * sum(S_i from i=1 to P)
Where S_i represents the spread at time i, and P is the lookback period (default is 200 bars). The moving average provides a baseline for the normal spread behavior.
2. Standard Deviation:
The standard deviation (sigma_S) of the spread is calculated to measure the volatility of the spread:
sigma_S = sqrt((1/P) * sum((S_i - mu_S)^2 from i=1 to P))
3. Upper and Lower Bollinger Bands:
The upper and lower Bollinger Bands are derived by adding and subtracting a multiple of the standard deviation from the moving average. The number of standard deviations is determined by a user-defined parameter k (default is 2.618).
- Upper Band:
Upper Band = mu_S + (k * sigma_S)
- Lower Band:
Lower Band = mu_S - (k * sigma_S)
These bands provide a dynamic range within which the spread typically fluctuates. When the spread moves outside of these bands, it is considered overbought or oversold, potentially offering trading opportunities.
Local view
🔶 Entry Conditions:
- Long Entry: A long position is triggered when the spread crosses below the lower Bollinger Band, indicating that the spread has become oversold and is likely to revert upward.
Spread < Lower Band
- Short Entry: A short position is triggered when the spread crosses above the upper Bollinger Band, indicating that the spread has become overbought and is likely to revert downward.
Spread > Upper Band
🔶 Risk Management and Profit-Taking:
The strategy incorporates multi-step take profits to lock in gains as the trade moves in favor. The position is gradually reduced at predefined profit levels, reducing risk while allowing part of the trade to continue running if the price keeps moving favorably.
Additionally, the strategy uses a hold period exit mechanism. If the trade does not hit any of the take-profit levels within a certain number of bars, the position is closed automatically to avoid excessive exposure to market risks.
█ Trade Direction
The trade direction is based on deviations of the spread from its historical norm:
- Long Trade: The strategy enters a long position when the spread crosses below the lower Bollinger Band, signaling an oversold condition where the spread is expected to narrow.
- Short Trade: The strategy enters a short position when the spread crosses above the upper Bollinger Band, signaling an overbought condition where the spread is expected to widen.
These entries rely on the assumption of mean reversion, where extreme deviations from the average spread are likely to revert over time.
█ Usage
The Bitcoin CME-Spot Bollinger Bands Strategy is ideal for traders looking to capitalize on price inefficiencies between Bitcoin futures and spot markets. It’s especially useful in volatile markets where large deviations between futures and spot prices occur.
- Market Conditions: This strategy is most effective in correlated markets, like CME futures and spot Bitcoin. Traders can adjust the Bollinger Bands period and standard deviation multiplier to suit different volatility regimes.
- Backtesting: Before deployment, backtesting the strategy across different market conditions and timeframes is recommended to ensure robustness. Adjust the take-profit steps and hold periods to reflect the trader’s risk tolerance and market behavior.
█ Default Settings
The default settings provide a balanced approach to spread trading using Bollinger Bands but can be adjusted depending on market conditions or personal trading preferences.
🔶 Bollinger Bands Period (200 bars):
This defines the number of bars used to calculate the moving average and standard deviation for the Bollinger Bands. A longer period smooths out short-term fluctuations and focuses on larger, more significant trends. Adjusting the period affects the responsiveness of the strategy:
- Shorter periods (e.g., 100 bars): Makes the strategy more reactive to short-term market fluctuations, potentially generating more signals but increasing the risk of false positives.
- Longer periods (e.g., 300 bars): Focuses on longer-term trends, reducing the frequency of trades and focusing only on significant deviations.
🔶 Standard Deviation Multiplier (2.618):
The multiplier controls how wide the Bollinger Bands are around the moving average. By default, the bands are set at 2.618 standard deviations away from the average, ensuring that only significant deviations trigger trades.
- Higher multipliers (e.g., 3.0): Require a more extreme deviation to trigger trades, reducing trade frequency but potentially increasing the accuracy of signals.
- Lower multipliers (e.g., 2.0): Make the bands narrower, increasing the number of trade signals but potentially decreasing their reliability.
🔶 Take-Profit Levels:
The strategy has four take-profit levels to gradually lock in profits:
- Level 1 (3%): 25% of the position is closed at a 3% profit.
- Level 2 (8%): 20% of the position is closed at an 8% profit.
- Level 3 (14%): 15% of the position is closed at a 14% profit.
- Level 4 (21%): 10% of the position is closed at a 21% profit.
Adjusting these take-profit levels affects how quickly profits are realized:
- Lower take-profit levels: Capture gains more quickly, reducing risk but potentially cutting off larger profits.
- Higher take-profit levels: Let trades run longer, aiming for bigger gains but increasing the risk of price reversals before profits are locked in.
🔶 Hold Days (20 bars):
The strategy automatically closes the position after 20 bars if none of the take-profit levels are hit. This feature prevents trades from being held indefinitely, especially if market conditions are stagnant. Adjusting this:
- Shorter hold periods: Reduce the duration of exposure, minimizing risks from market changes but potentially closing trades too early.
- Longer hold periods: Allow trades to stay open longer, increasing the chance for mean reversion but also increasing exposure to unfavorable market conditions.
By understanding how these default settings affect the strategy’s performance, traders can optimize the Bitcoin CME-Spot Bollinger Bands Strategy to their preferences, adapting it to different market environments and risk tolerances.
VolCorrBeta [NariCapitalTrading]Indicator Overview: VolCorrBeta
The VolCorrBeta indicator is designed to analyze and interpret intermarket relationships. This indicator combines volatility, correlation, and beta calculations to provide a comprehensive view of how certain assets (BTC, DXY, CL) influence the ES futures contract (I tailored this indicator to the ES contract, but it will work for any symbol).
Functionality
Input Symbols
BTCUSD : Bitcoin to USD
DXY : US Dollar Index
CL1! : Crude Oil Futures
ES1! : S&P 500 Futures
These symbols can be customized according to user preferences. The main focus of the indicator is to analyze how the price movements of these assets correlate with and lead the price movements of the ES futures contract.
Parameters for Calculation
Correlation Length : Number of periods for calculating the correlation.
Standard Deviation Length : Number of periods for calculating the standard deviation.
Lookback Period for Beta : Number of periods for calculating beta.
Volatility Filter Length : Length of the volatility filter.
Volatility Threshold : Threshold for adjusting the lookback period based on volatility.
Key Calculations
Returns Calculation : Computes the daily returns for each input symbol.
Correlation Calculation : Computes the correlation between each input symbol's returns and the ES futures contract returns over the specified correlation length.
Standard Deviation Calculation : Computes the standard deviation for each input symbol's returns and the ES futures contract returns.
Beta Calculation : Computes the beta for each input symbol relative to the ES futures contract.
Weighted Returns Calculation : Computes the weighted returns based on the calculated betas.
Lead-Lag Indicator : Calculates a lead-lag indicator by averaging the weighted returns.
Volatility Filter : Smooths the lead-lag indicator using a simple moving average.
Price Target Estimation : Estimates the ES price target based on the lead-lag indicator (the yellow line on the chart).
Dynamic Stop Loss (SL) and Take Profit (TP) Levels : Calculates dynamic SL and TP levels using volatility bands.
Signal Generation
The indicator generates buy and sell signals based on the filtered lead-lag indicator and confirms them using higher timeframe analysis. Signals are debounced to reduce frequency, ensuring that only significant signals are considered.
Visualization
Background Coloring : The background color changes based on the buy and sell signals for easy visualization (user can toggle this on/off).
Signal Labels : Labels with arrows are plotted on the chart, showing the signal type (buy/sell), the entry price, TP, and SL levels.
Estimated ES Price Target : The estimated price target for ES futures is plotted on the chart.
Correlation and Beta Dashboard : A table displayed in the top right corner shows the current correlation and beta values for relative to the ES futures contract.
Customization
Traders can customize the following parameters to tailor the indicator to their specific needs:
Input Symbols : Change the symbols for BTC, DXY, CL, and ES.
Correlation Length : Adjust the number of periods used for calculating correlation.
Standard Deviation Length : Adjust the number of periods used for calculating standard deviation.
Lookback Period for Beta : Change the lookback period for calculating beta.
Volatility Filter Length : Modify the length of the volatility filter.
Volatility Threshold : Set a threshold for adjusting the lookback period based on volatility.
Plotting Options : Customize the colors and line widths of the plotted elements.
Henry's Vwap-VolumeThis Indicator is meant to provide Futures Volume and Vwap Signal in spot charts of Nifty and Banknifty Traders.
Concepts and Features of this indicators are as follows :
1) Now u don't have to select and change to futures scrip often or have both spot and futures chart in same window to watch the Futures Volume and Vwap.
2) U get Both Volume and Vwap signal as a indicator in single pane.
3) Its for Nifty and Banknifty Traders specially.
4)Volume with moving average is from the futures chart of banknifty or nifty,also may select any other futures script as per ur need.
(MOVING AVERAGE of VOLUME is plotted in Blue columns over the Volume.)
5)Vwap signal is also derived from the futures chart of banknifty or nifty,also may select any other futures script as per ur need.
(VWAP SIGNAL is plotted in GREEN or RED as background.If futures price higher than Vwap then Green , opposite for Red. )
6)The idea of this script is to give extra confirmation of a clear down or uptrend while u are in the spot chart.(nifty and banknifty)
7) U can select and change any scrip u like.But I urge to use futures chart of banknifty or nifty.
I hope this indicator will help a lot of retail investor save their hard earned money in the stock market and benefit from Mr. NK's strategy.
How to Use :
Go Long - when background is Green.
Go Short -when background is Red.
(Also take confirmation from the blue columns -moving average of volumes.volume higher or less than it.)
Limitations :
U can only use it for intraday,less than 1D timeframe.
Will not work in sideways market.
Take help of other indicators also like Rsi,adx,etc.
Best of Luck,
Henry
Crude Roll Trade SimulatorEDIT : The screen cap was unintended with the script publication. The yellow arrow is pointing to a different indicator I wrote. The "Roll Sim" indicator is shown below that one. Yes I could do a different screen cap, but then I'd have to rewrite this and frankly I don't have time. END EDIT
If you have ever wanted to visualize the contango / backwardation pressure of a roll trade, this script will help you approximate it.
I am writing this description in haste so go with me on my rough explanations.
A "roll trade" is one involving futures that are continually rolled over into future months. Popular roll trade instruments are USO (oil futures) and UVXY (volatility futures).
Roll trades suffer hits from contango but get rewarded in periods of backwardation. Use this script to track the contango / backwardation pressure on what you are trading.
That involves identifying and providing both the underlying indexes and derivatives for both the front and back month of the roll trade. What does that mean? Well the defaults simulate (crudely) the UVXY roll trade: The folks at Proshares buy futures that expire 60 days away and then sell those 30 days later as short term futures (again, this is a crude description - see the prospectus) and we simulate that by providing the Roll Sim indicator the symbols VIX and VXV along with VIXY and VIXM. We also provide the days between the purchase and sale of the rolled futures contract (in sessions, which is 22 days by my reckoning).
The script performs ema smoothing and plots both the index lines (VIX and VXV as solid lines in our case) and the derivatives (VIXY and VIXM as dotted lines in our case) with the line graphs offset by the number of sessions between the buy and sell. The gap you see represents the contango / backwardation the derivative roll trades are experiencing and gives you an idea how much movement has to happen for that gap to widen, contract or even invert. The background gets painted red in periods of backwardation (when the longer term futures cost less than when sold as short term futures).
Fortunately indexes are calibrated to the same underlying factors, so their values relative to each other are meaningful (ie VXV of 18 and VIX of 15 are based on the same calculation on premiums for S&P500 symbols, with VXV being normally higher for time value). That means the indexes graph well without and adjustments needed. Unfortunately derivatives suffer contango / backwardation at different rates so the value of VIXY vs VIXM isn't really meaningful (VIXY may take a reverse split one year while VIXM doesn't) ... what is meaningful is their relative change in value day to day. So I have included a "front month multiplier" which can be used to get the front month line "moved up or down" on the screen so it can be compared to the back month.
As a practical matter, I have come to hide the lines for the derivatives (like VIXY and VIXM) and just focus on the gap changes between the indexes which gives me an idea of what is going on in the market and what contango/backwardation pressure is likely to exist next week.
Hope it is useful to you.
Get_rich_aggressively# Get_rich_aggressively - Professional Order Flow Indicator
## 📊 Overview
**Get_rich_aggressively (GRA)** is a professional-grade order flow indicator designed for futures and crypto traders who understand Auction Market Theory and want to identify high-probability setups with exceptional risk-to-reward ratios (3:1 to 5:1).
This indicator answers the two most critical questions in trading:
1. **Who is in control?** Bulls or Bears based on volume delta and aggression
2. **Where are traders trapped?** Identifying failed breakouts that lead to explosive moves
Built specifically for **NQ (Nasdaq 100 Futures)**, **GC (Gold Futures)**, and **BTC (Bitcoin)**, with optimized settings for each instrument's unique volatility profile.
---
## 🎯 Core Concepts
### Volume Delta & Imbalance
The indicator calculates **buying pressure vs selling pressure** within each candle using intrabar analysis. When one side significantly overpowers the other, an **imbalance** exists—these are the moments where price moves with conviction.
**Imbalance Ratio Interpretation:**
- `1.5:1` → Moderate imbalance (tradeable)
- `2.0:1` → Strong imbalance (high conviction)
- `3.0:1+` → Extreme imbalance (institutional activity)
### Aggression Detection
The indicator identifies which side is **aggressing** (hitting market orders) vs **absorbing** (resting limit orders). When aggressive buyers overwhelm sellers, price moves UP. When aggressive sellers overwhelm buyers, price moves DOWN.
### Trap Detection
**Bull Traps** and **Bear Traps** are failed breakouts where traders get caught on the wrong side. These setups often lead to explosive reversals as trapped traders are forced to exit.
---
## 🔧 How To Use
### Visual Elements
| Element | Meaning |
|---------|---------|
| **Green Bubbles** (below bar) | Bullish volume aggression - buyers winning |
| **Red Bubbles** (above bar) | Bearish volume aggression - sellers winning |
| **▲ Triangle Up** | Long signal (3:1 or 4:1+ R:R) |
| **▼ Triangle Down** | Short signal (3:1 or 4:1+ R:R) |
| **BT / BT!** | Bull Trap (short opportunity) |
| **BrT / BrT!** | Bear Trap (long opportunity) |
| **Diamond** | Absorption candle (institutional activity) |
| **Colored Candles** | Green = positive delta, Red = negative delta |
### Info Panel (Top Right)
| Field | Description |
|-------|-------------|
| **Delta** | Current bar's volume delta (buy - sell pressure) |
| **Vol** | Volume ratio vs 20-period average |
| **Imb** | Imbalance ratio (who's winning) |
| **Ctrl** | Who's in control: BULLS / BEARS |
| **CVD** | Cumulative Volume Delta for session |
| **Sess** | Current session (LDN! = London Open, NY! = NY Open) |
| **Mkt** | Market condition: CHOP / FV / IMB! / OK |
| **Sig** | Active signal if any |
### Signal Hierarchy
**High Conviction Signals (4:1+ R:R):**
- Displayed as `▲ 4:1+` or `▼ 4:1+`
- Require: Strong imbalance (2:1+) + Big volume (2.5x+) + Delta trend confirmation
- Best during London Open or NY Open sessions
**Standard Signals (3:1 R:R):**
- Displayed as `▲ 3:1` or `▼ 3:1`
- Require: Moderate imbalance (1.5:1+) + Volume spike (1.5x+)
- Good any time market is not choppy
**Trap Signals:**
- `BT!` = Strong Bull Trap → SHORT
- `BrT!` = Strong Bear Trap → LONG
- Occur at swing highs/lows with rejection wicks
---
## ⚙️ Recommended Settings by Instrument
### 📈 NQ (Nasdaq 100 E-mini Futures)
NQ is highly liquid with clear institutional footprints. The default settings work excellently.
```
═══════════ VOLUME ANALYSIS ═══════════
Volume MA Length: 20
Volume Spike Threshold: 1.5
Big Trade Threshold: 2.5
Extreme Volume Threshold: 4.0
═══════════ IMBALANCE DETECTION ═══════════
Imbalance Ratio Threshold: 1.5
Strong Imbalance Threshold: 2.0
Delta Confirmation Bars: 3
Fair Value Range (%): 0.3
═══════════ TRAP DETECTION ═══════════
Swing Lookback Period: 20
Minimum Wick Ratio: 0.4
Max Body Ratio (Absorption): 0.35
═══════════ SESSION SETTINGS ═══════════
Timezone: America/New_York
London Open Window: 0300-0500
NY Open Window: 0930-1130
Only Signal During Key Sessions: OFF (or ON for higher conviction)
═══════════ TIMEFRAME SETTINGS ═══════════
Analysis Timeframe: 1 (1-minute intrabar analysis)
Use Intrabar Analysis: ON
```
**Best Timeframes for NQ:**
- **1-minute**: Scalping, quick entries
- **5-minute**: Day trading (RECOMMENDED)
- **15-minute**: Swing entries within day
**NQ Trading Tips:**
- Most reliable signals occur during **9:30-11:30 AM EST** (NY Open)
- Watch for traps at **overnight high/low** levels
- Volume spikes of **3x+** often precede 10-20 point moves
- Avoid trading during **12:00-2:00 PM EST** (lunch chop)
---
### 🥇 GC (Gold Futures)
Gold has different volatility patterns. Increase thresholds slightly to filter noise.
```
═══════════ VOLUME ANALYSIS ═══════════
Volume MA Length: 20
Volume Spike Threshold: 1.8 ← Increased (gold has more noise)
Big Trade Threshold: 3.0 ← Increased
Extreme Volume Threshold: 5.0 ← Increased
═══════════ IMBALANCE DETECTION ═══════════
Imbalance Ratio Threshold: 1.6 ← Slightly higher
Strong Imbalance Threshold: 2.2 ← Slightly higher
Delta Confirmation Bars: 4 ← More confirmation needed
Fair Value Range (%): 0.4 ← Gold chops more
═══════════ TRAP DETECTION ═══════════
Swing Lookback Period: 25 ← Wider swings
Minimum Wick Ratio: 0.45 ← Bigger wicks needed
Max Body Ratio (Absorption): 0.30 ← Tighter for absorption
═══════════ SESSION SETTINGS ═══════════
Timezone: America/New_York
London Open Window: 0300-0500 ← Gold moves well here
NY Open Window: 0830-1030 ← Earlier due to economic news
Only Signal During Key Sessions: ON ← Recommended for GC
═══════════ TIMEFRAME SETTINGS ═══════════
Analysis Timeframe: 1
Use Intrabar Analysis: ON
```
**Best Timeframes for GC:**
- **5-minute**: Day trading (RECOMMENDED)
- **15-minute**: Position entries
- **1-hour**: Swing trading
**Gold Trading Tips:**
- Gold reacts strongly to **economic data releases** (8:30 AM EST)
- **London session** (3-5 AM EST) often sets the daily direction
- Watch for traps at **round numbers** ($2000, $2050, etc.)
- Gold respects **previous day high/low** as key levels
- Absorption candles near support/resistance signal reversals
---
### ₿ BTC (Bitcoin)
Bitcoin trades 24/7 with unique session dynamics. Adjust for higher volatility.
```
═══════════ VOLUME ANALYSIS ═══════════
Volume MA Length: 30 ← Longer average (24/7 market)
Volume Spike Threshold: 2.0 ← Higher threshold (crypto volatility)
Big Trade Threshold: 3.5 ← Higher for significance
Extreme Volume Threshold: 6.0 ← Much higher for crypto
═══════════ IMBALANCE DETECTION ═══════════
Imbalance Ratio Threshold: 1.7 ← Higher due to volatility
Strong Imbalance Threshold: 2.5 ← Higher for conviction
Delta Confirmation Bars: 3
Fair Value Range (%): 0.5 ← BTC ranges more
═══════════ TRAP DETECTION ═══════════
Swing Lookback Period: 30 ← Wider lookback
Minimum Wick Ratio: 0.5 ← BTC has massive wicks
Max Body Ratio (Absorption): 0.25 ← Tighter (many dojis in crypto)
═══════════ SESSION SETTINGS ═══════════
Timezone: America/New_York
London Open Window: 0300-0500 ← European session start
NY Open Window: 0930-1130 ← US session (big moves)
Only Signal During Key Sessions: OFF ← BTC moves 24/7
═══════════ TIMEFRAME SETTINGS ═══════════
Analysis Timeframe: 1
Use Intrabar Analysis: ON
```
**Best Timeframes for BTC:**
- **5-minute**: Active trading
- **15-minute**: Day trading (RECOMMENDED)
- **1-hour**: Swing trading
- **4-hour**: Position trading
**Bitcoin Trading Tips:**
- **US Session** (9:30 AM - 4:00 PM EST) has highest volume
- **Asian Session** (8 PM - 4 AM EST) often consolidates
- Watch for traps at **psychological levels** ($60K, $65K, $70K, etc.)
- **Funding rate flips** often coincide with trap signals
- Weekend volume is lower—signals less reliable
---
## 📋 Trading Playbook
### Setup 1: High Conviction Imbalance Entry
**Conditions:**
- ▲ or ▼ signal appears with "4:1+" label
- Info panel shows "Ctrl: BULLS" or "Ctrl: BEARS"
- Info panel shows "Mkt: IMB!"
- During active session (LDN! or NY!)
**Entry:** Market order on signal bar close
**Stop Loss:** Beyond the signal candle's wick
**Take Profit:** 4:1 risk-to-reward minimum
---
### Setup 2: Trap Reversal
**Conditions:**
- BT! (Bull Trap) or BrT! (Bear Trap) appears
- Signal occurs at swing high/low
- Volume spike confirms (2x+ average)
**Entry:**
- Bull Trap → SHORT on close below signal bar
- Bear Trap → LONG on close above signal bar
**Stop Loss:** Beyond the trap wick
**Take Profit:** Previous swing level (3:1+ R:R typical)
---
### Setup 3: Absorption Reversal
**Conditions:**
- Diamond marker appears (absorption)
- At key support/resistance level
- Followed by opposite-colored candle with volume
**Entry:** On confirmation candle close
**Stop Loss:** Beyond absorption candle
**Take Profit:** 2:1 minimum
---
### Setup 4: Session Open Momentum
**Conditions:**
- "L" (London) or "N" (NY) session marker appears
- First 30 minutes show clear delta direction
- Imbalance ratio > 1.5:1
**Entry:** With the dominant delta direction
**Stop Loss:** Session open price
**Take Profit:** Previous day high/low or 3:1 R:R
---
## ⚠️ When NOT to Trade
Avoid taking signals when:
1. **Info panel shows "Mkt: CHOP"** - Market is ranging without conviction
2. **Info panel shows "Mkt: FV"** - Fair value zone, expect mean reversion
3. **Ctrl shows "---"** - Neither side in control
4. **During lunch hours** (12:00-2:00 PM EST for futures)
5. **Before major news** (FOMC, NFP, CPI)
6. **Low volume sessions** (holidays, weekends for futures)
---
## 🔔 Alerts Setup
The indicator includes pre-built alerts. To set them up:
1. Click the "Alerts" button (clock icon) in TradingView
2. Select "Get_rich_aggressively" as condition
3. Choose from available alerts:
- **EXTREME LONG** - 4:1+ bullish setup
- **EXTREME SHORT** - 4:1+ bearish setup
- **HIGH CONV LONG** - 3:1 bullish setup
- **HIGH CONV SHORT** - 3:1 bearish setup
- **BULL TRAP** - Failed breakout, short opportunity
- **BEAR TRAP** - Failed breakdown, long opportunity
- **LONDON OPEN** - Session notification
- **NY OPEN** - Session notification
---
## 📚 Understanding the Logic
### Volume Delta Calculation
The indicator uses **intrabar analysis** to calculate precise volume delta:
```
For each lower-timeframe bar within the current bar:
Buy Pressure = ((Close - Low) / Range) × Volume
Sell Pressure = ((High - Close) / Range) × Volume
Delta = Buy Pressure - Sell Pressure
Total Delta = Sum of all intrabar deltas
```
This method is more accurate than simple "green candle = buying" logic because it captures the **internal auction** within each candle.
### Imbalance Ratio
```
Bullish Imbalance = Buy Pressure / Sell Pressure
Bearish Imbalance = Sell Pressure / Buy Pressure
If ratio ≥ 1.5 AND volume spike → Standard signal
If ratio ≥ 2.0 AND big volume → High conviction signal
```
### Trap Detection
```
Bull Trap =
Price breaks ABOVE swing high +
Closes BACK BELOW swing high +
Upper wick ≥ 40% of candle range +
Volume spike present
Bear Trap =
Price breaks BELOW swing low +
Closes BACK ABOVE swing low +
Lower wick ≥ 40% of candle range +
Volume spike present
```
---
## 💡 Pro Tips
1. **Combine with market structure** - Signals at key S/R levels are stronger
2. **Watch CVD divergence** - If CVD trends opposite to price, reversal likely
3. **Stack confluences** - Trap + Absorption + Session Open = highest probability
4. **Scale in** - Enter 50% on signal, add on confirmation
5. **Use session filter** - Enable "Only Signal During Key Sessions" for cleaner signals
6. **Check higher timeframe** - Ensure signal aligns with HTF trend/bias
---
## ⚠️ Disclaimer
This indicator is a tool to assist your trading decisions, not a guarantee of profits. Past performance does not indicate future results. Always:
- Use proper risk management (1-2% max per trade)
- Paper trade before going live
- Understand the instrument you're trading
- Never risk more than you can afford to lose
---
## 🔄 Version History
**v1.0** - Initial release
- Volume delta calculation with intrabar analysis
- Imbalance detection and signals
- Bull/Bear trap identification
- Absorption candle detection
- Session filtering (London/NY)
- Real-time info panel
- Comprehensive alert system
---
## 📬 Support
If you have questions or suggestions, leave a comment below or send me a message.
**Happy Trading! Let's Get Rich Aggressively! 🚀**
---
*This indicator is inspired by Auction Market Theory, Order Flow concepts, and professional tools like DeepCharts, Sierra Chart, and Bookmap. It brings institutional-grade analysis to TradingView.*
Donchian Predictive Channel (Zeiierman)█ Overview
Donchian Predictive Channel (Zeiierman) extends the classic Donchian framework into a predictive structure. It does not just track where the range has been; it projects where the Donchian mid, high, and low boundaries are statistically likely to move based on recent directional bias and volatility regime.
By quantifying the linear drift of the Donchian midline and the expansion or compression rate of the Donchian range, the indicator generates a forward propagation cone that reflects the prevailing trend and volatility state. This produces a cleaner, more analytically grounded projection of future price corridors, and it remains fully aligned with the signal precision of the underlying Donchian logic.
█ How It Works
⚪ Donchian Core
The script first computes a standard Donchian Channel over a configurable Length:
Upper Band (dcHi) – highest high over the lookback.
Lower Band (dcLo) – lowest low over the lookback.
Midline (dcMd) – simple midpoint of upper and lower: (dcHi + dcLo)/ 2.
f_getDonchian(length) =>
hi = ta.highest(high, length)
lo = ta.lowest(low, length)
md = (hi + lo) * 0.5
= f_getDonchian(lenDC)
⚪ Slope Estimation & Range Dynamics
To turn the Donchian Channel into a predictive model, the script measures how both the midline and the range are changing over time:
Midline Slope (mSl) – derived from a 1-bar difference in linear regression of the midline.
Range Slope (rSl) – derived from a 1-bar difference in linear regression of the Donchian range (dcHi − dcLo).
This pair describes both directional drift (uptrend vs. downtrend) and range expansion/compression (volatility regime).
f_getSlopes(midLine, rngVal, length) =>
mSl = ta.linreg(midLine, length, 0) - ta.linreg(midLine, length, 1)
rSl = ta.linreg(rngVal, length, 0) - ta.linreg(rngVal, length, 1)
⚪ Forward Projection Engine
At the last bar, the indicator constructs a set of forward points for the mid, upper, and lower projections over Forecast Bars:
The midline is projected linearly using the midline slope per bar.
The range is adjusted using the range slope per bar, creating either a widening cone (expansion) or a tightening cone (compression).
Upper and lower projections are then anchored around the projected midline, with logic that keeps the structure consistent and prevents pathological flips when slope changes sign.
f_generatePoints(hi0, md0, lo0, steps, midSlp, rngSlp) =>
upPts = array.new()
mdPts = array.new()
dnPts = array.new()
fillPts = array.new()
hi_vals = array.new_float()
md_vals = array.new_float()
lo_vals = array.new_float()
curHiLocal = hi0
curLoLocal = lo0
curMidLocal = md0
segBars = math.floor(steps / 3)
segBars := segBars < 1 ? 1 : segBars
for b = 0 to steps
mdProj = md0 + midSlp * b
prevRange = curHiLocal - curLoLocal
rngProj = prevRange + rngSlp * b
hiTemp = 0.0
loTemp = 0.0
if midSlp >= 0
hiTemp := math.max(curHiLocal, mdProj + rngProj * 0.5)
loTemp := math.max(curLoLocal, mdProj - rngProj * 0.5)
else
hiTemp := math.min(curHiLocal, mdProj + rngProj * 0.5)
loTemp := math.min(curLoLocal, mdProj - rngProj * 0.5)
hiProj = hiTemp < mdProj ? curHiLocal : hiTemp
loProj = loTemp > mdProj ? curLoLocal : loTemp
if b % segBars == 0
curHiLocal := hiProj
curLoLocal := loProj
curMidLocal := mdProj
array.push(hi_vals, curHiLocal)
array.push(md_vals, curMidLocal)
array.push(lo_vals, curLoLocal)
array.push(upPts, chart.point.from_index(bar_index + b, curHiLocal))
array.push(mdPts, chart.point.from_index(bar_index + b, curMidLocal))
array.push(dnPts, chart.point.from_index(bar_index + b, curLoLocal))
ptSet.new(upPts, mdPts, dnPts)
⚪ Rejection Signals
The script also tracks failed Donchian breakouts and marks them as potential reversal/reversion cues:
Signal Down: Triggered when price makes an attempt above the upper Donchian band but then pulls back inside and closes above the midline, provided enough bars have passed since the last signal.
Signal Up: Triggered when price makes an attempt below the lower Donchian band but then snaps back inside and closes below the midline, also requiring sufficient spacing from the previous signal.
// Base signal conditions (unfiltered)
bearCond = high < dcHi and high >= dcHi and close > dcMd and bar_index - lastMarker >= lenDC
bullCond = low > dcLo and low <= dcLo and close < dcMd and bar_index - lastMarker >= lenDC
// Apply MA filter if enabled
if signalfilter
bearCond := bearCond and close < ma // Bearish only below MA
bullCond := bullCond and close > ma // Bullish only above MA
signalUp := false
signalDn := false
if bearCond
lastMarker := bar_index
signalDn := true
if bullCond
lastMarker := bar_index
signalUp := true
█ How to Use
The Donchian Predictive Channel is designed to outline possible future price trajectories. Treat it as a directional guide, not a fixed prediction tool.
⚪ Map Future Support & Resistance
Use the projected upper and lower paths as dynamic future reference levels:
Projected upper band ≈ is likely a resistance corridor if the current trend and volatility persist.
Projected lower band ≈ likely support corridor or expected downside range.
⚪ Trend Path & Volatility Cone
Because the projection is driven by midline and range slopes, the channel behaves like a trend + volatility cone:
Steep positive midline slope + expanding range → accelerating, high-volatility trend.
Flat midline + compressing range → coiling/contracting regime ahead of potential expansion.
This helps you distinguish between a gentle drift and an aggressive move that likely needs more risk buffer.
⚪ Reversion & Rejection Signals
The Donchian-based signals are especially useful for mean-reversion and fade-style trades.
A Signal Down near the upper band can mark a failed breakout and a potential rotation back toward the midline or the lower projected band.
A Signal Up near the lower band can flag a failed breakdown and a potential snap-back up the channel.
When Filter Signals is enabled, these signals are only generated when they align with the chart’s directional bias as defined by the moving average. Bullish signals are allowed only when the price is above the MA, and bearish signals only when the price is below it.
This reduces noise and helps ensure that reversions occur in harmony with the prevailing trend environment.
█ Settings
Length – Donchian lookback length. Higher values produce a smoother channel with fewer but more stable signals. Lower values make the channel more reactive and increase sensitivity at the cost of more noise.
Forecast Bars – Number of bars used for projecting the Donchian channel forward.
Higher values create a broader, longer-term projection. Lower values focus on short-horizon price path scenarios.
Filter Signals – Enables directional filtering of Donchian signals using the selected moving average. When ON, bullish signals only trigger when the price is above the MA, and bearish signals only trigger when the price is below it. This helps reduce noise and aligns reversions with the broader trend context.
Moving Average Type – The type of moving average used for signal filtering and optional plotting.
Choose between SMA, EMA, WMA, or HMA depending on desired responsiveness. Faster averages (EMA, HMA) react quickly, while slower ones (SMA, WMA) smooth out short-term noise.
Moving Average Length – Lookback length of the moving average. Higher values create a slower, more stable trend filter. Lower values track price more tightly and can flip the directional bias more frequently.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Camarilla Pivot Plays (Lite) [BruzX]█ OVERVIEW
This indicator implements the Camarilla Pivot Points levels and a system for suggesting particular plays. It only 3rd, 4th, and 6th levels, as these are the only ones used by the system. It also optionally shows the Central Pivot Range, which is in fact between S2 and R2. In total, there are 12 possible plays, grouped into two groups of six. The algorithm evaluates in real-time which plays fulfil their precondition and shows the candidate plays. The user must then decide if and when to take the play.
█ CREDITS
The Camarilla pivot plays are defined in a strategy developed by Thor Young, and the whole system is explained in his book "A Complete Day Trading System". This description is self-sufficient for effective use.
█ FEATURES
Display the 3rd, 4th and 6th Camarilla pivot levels
Works for stocks, futures, indices, forex and crypto
Automatically switches between RTH and ETH data based on criteria defined by the system.
Option to force RTH/ETH data and force a close price to be used in the calculation.
Preconditions for the plays can be toggled on/off
Works correctly on both RTH and ETH charts
Well-documented options tooltips
Well-documented and high-quality open-source code for those who are interested
█ HOW TO USE
The defaults work well; at a minimum, just add the indicator and watch the plays being called. For US futures, you will probably want to chat the "Timezone for sessions" to New York and the regular session times to 09:30 - 16:00. The following diagram shows its key features.
By default, the indicator draws plays 1 days back; this can be changed up to 20 days. The labels can be shifted left/right using the "label offset" option to avoid overlapping with other labels in this indicator or those of another indicator.
An information box at the top-right of the chart shows:
The data currently in use for the main pivots. This can switch in the pre-market if the H/L range exceeds the previous day's H/L, and if it does, you will see that switch at the time that it happens
Whether the current day's pivots are in a higher or lower range compared to the previous day's.
The width of the pivots compared to the previous day
The current candidate plays fulfilling preconditions. You then need to watch the price action to decide whether to take the play.
The resistance pivots are all drawn in the same colour (red by default), as are the support pivots (green by default). You can change the resistance and support colours, but it is not possible to have different colours for different levels of the same kind.
█ CONCEPTS
The indicator is focused around daily Camarilla pivots and evaluates the preconditions for 12 possible plays: 6 when in a higher range, 6 when in a lower range. The plays are labelled by two letters—the first indicates the range, the second indicates the play—as shown in this diagram:
The pivots can be calculated using only RTH (Regular Trading Hours) data, or ETH (Extended Trading Hours) data, which includes the pre-market and post-market. The indicator implements logic to automatically choose the correct data, based on the rules defined by the strategy. This is user-overridable. With the default options, ETH will be used when the H/L range in the previous day's post-market or current day's pre-market exceeds that of the previous day's regular market. In auto mode, the chosen pivots are considered the main pivots for that day and are the ones used for play evaluation. The "other" pivots can also be shown—"other" here meaning using ETH data when the main pivots use RTH data, and vice versa.
The plays must fulfil a set of preconditions. There are preconditions for valid region and range, price sweeps into levels, correct pivot width, opening position, price action, and whether neutral range plays and premarket plays are enabled. When all the preconditions are fulfilled, the play will be shown as a candidate.
█ NOTE FOR FUTURES
Futures don't officially have a pre-market or post-market like equities. Let's take ES on CME as an example. It trades from 18:00 ET Sunday to 17:00 Friday (ET), with a daily pause between 17:00 and 18:00 ET. However, most of the trading activity is done between 09:30 and 16:00, which you can tell from the volume spikes at those times, and this coincides with NYSE/NASDAQ regular hours. So we define a pseudo-pre-market from 18:00 the previous day to 09:30 on the current day, then a pseudo-regular market from 08:30 to 16:00, then a pseudo-post-market from 16:00 to 17:00. The indicator then works exactly the same as with equities—all the options behave the same, just with different session times defined for the pre-, regular, and post-market, with "RTH" meaning just the regular market and "ETH" meaning all three.
█ LIMITATIONS
The pivots are very close to those shown in DAS Trader Pro. They are not to-the-cent exact, but within a few cents. The reasons are:
TradingView provides free real-time data from CBOE One, not full exchange data (you can pay for this though, and it's not expensive), and
the close/high/low are taken from the intraday timeframe you are currently viewing, not daily data—which are very close, but often not exactly the same. For example, the high on the daily timeframe may differ slightly from the daily high you'll see on an intraday timeframe.
Despite these caveats, occasionally large spikes will be seem in one platform and not the other (even with paid data), or the spikes will reach significantly difference prices. Where these spikes create the daily high or low, this can cause significantly different pivots levels. The more traded the stock is, the less the difference tends to be. Highly traded stocks are usually within a few cents (but even they occasionally have large differences in spikes). There is nothing that can be done about this.
The 6th Camarilla level does not have a standard definition and may not match the level shown on other platforms. It does match the definition used by DAS Trader Pro.
Replay mode for stocks does not work correctly. This is due to some important Pine Script variables provided by the TradingView platform and used by the script not being assigned correct values in replay mode. Futures do not use these variables, so they should work in replay mode.
The indicator is an intraday indicator (despite also being able to show weekly and monthly pivots on an intraday chart). It deactivates on a daily timeframe and higher. Sub-minute timeframes are also not supported.
The indicator was developed and tested for US/European stocks, US futures and EURUSD forex and BTCUSD. It should work as intended for stocks and futures in different countries, and for all forex and crypto, but this is tested as much as the security it was developed for.
█ DISCLAIMER
This indicator is provided for information only and should not be used in isolation without a good understand of the system and without considering other factors. You should not take trades using real money based solely on what this indicator says. Any trades you take are entirely at your own risk.
PG ATM Strike Line with Call & Put PremiumsPine Script: ATM Strike Line with Call & Put Premiums (Simplified)This Pine Script for TradingView displays the At-The-Money (ATM) strike price, futures price, call/put premiums (time value), and two ratios—Premium Ratio (PR) and Volume Ratio (VR)—for a user-selected underlying asset (e.g., NIFTY, BANKNIFTY, or stocks). It helps traders gauge near-term market direction using options data.How the Script WorksInputs:Expiry: Select year (e.g., '25), month (01–12), day (01–31) for option expiry (e.g., '251028').
Timeframe: Choose data timeframe (e.g., Daily, 15-min).
Symbol: Auto-detects chart symbol or select from Indian indices/stocks.
Strike: Auto-ATM (based on futures) or manual strike input.
Interval: Auto (e.g., 100 for NIFTY) or custom strike interval.
Colors: Customizable for ATM line, labels (Futures Price, CPR, PPR, VR, PR).
Calculations:Futures Price (FP): Fetches front-month futures price (e.g., NSE:NIFTY1!).
ATM Strike: Rounds futures price to nearest strike interval.
Option Data: Retrieves Last Traded Price (LTP) and volume for ATM call/put options (e.g., NSE:NIFTY251028C24200).
Call Premium (CPR): Call LTP minus intrinsic value (max(0, FP - Strike)).
Put Premium (PPR): Put LTP minus intrinsic value (max(0, Strike - FP)).
Premium Ratio (PR): PPR / CPR.
Volume Ratio (VR): Put Volume / Call Volume.
Visuals:Draws ATM strike line on chart.
Displays labels: FP (futures price), CPR (call premium), PPR (put premium), VR, PR.
VR/PR labels: Red (≥ 1.25, bearish), Green (≤ 0.75, bullish), Gray (0.75–1.25, neutral).
Updates on last confirmed bar to avoid redraws.
Using PR and VR for Market DirectionPremium Ratio (PR):PR ≥ 1.25 (Red): High put premiums suggest bearish sentiment (expect price drop).
PR ≤ 0.75 (Green): High call premiums suggest bullish sentiment (expect price rise).
0.75 < PR < 1.25 (Gray): Neutral, no clear direction.
Use: High PR favors bearish trades (e.g., buy puts); low PR favors bullish trades (e.g., buy calls).
Volume Ratio (VR):VR ≥ 1.25 (Red): High put volume indicates bearish activity.
VR ≤ 0.75 (Green): High call volume indicates bullish activity.
0.75 < VR < 1.25 (Gray): Neutral trading activity.
Use: High VR suggests bearish moves; low VR suggests bullish moves.
Combined Signals:High PR & VR: Strong bearish signal; consider put buying or call selling.
Low PR & VR: Strong bullish signal; consider call buying or put selling.
Mixed/Neutral: Use price action or support/resistance for confirmation.
Tips:Combine with technical analysis (e.g., trends, levels).
Match timeframe to trading horizon (e.g., 15-min for intraday).
Monitor FP for context; check volatility or news for accuracy.
ExampleNIFTY: FP = 24,237.50, ATM = 24,200, CPR = 120.25, PPR = 180.50, PR = 1.50 (Red), VR = 1.30 (Red).
Insight: High PR/VR suggests bearish bias; consider bearish trades if price nears resistance.
Action: Buy puts or exit longs, confirm with price action.
Conclusion: This script provides a concise tool for options traders, showing ATM strike, premiums, and PR/VR ratios. High PR/VR (≥ 1.25) signals bearish sentiment, low PR/VR (≤ 0.75) signals bullish sentiment, and neutral (0.75–1.25) suggests indecision. Combine with technical analysis for robust trading decisions in the Indian options market.
