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Candlestick analysis

Multi Time Frame Supporting Candles (with EMA/VWAP)Overview This script provides a clean and, most importantly, correctly-scaled Multi-Time-Frame (MTF) analysis panel. It displays the candles of a higher timeframe (e.g., 1-hour candles while you are on a 5-minute chart) in a separate pane below your main chart. Crucially, it solves a common Pine Script scaling issue, allowing you to plot indicators like EMA and VWAP from the higher timeframe alongside the candles without distorting the display. This tool is essential for traders who practice Multi-Time-Frame Analysis, helping you stay aware of the larger trend context while executing trades on a lower timeframe.
Wskaźnik Pine Script®
od noobgoo
Volume Pressure Analysis - Live DataVolume Pressure Gauge and Volume Percentage Indicator – Pine Script Guide This indicator provides a simplified, real-time visualization of both volume pressure (buy vs. sell activity) and today’s trading volume in comparison to historical averages. It is designed to help traders assess whether buyers or sellers dominate the current session and whether today’s volume is significant relative to recent behaviour. ________________________________________ Key Functional Segments 1. Inputs and Configuration Users can configure the length of the Simple Moving Average (SMA) used to calculate average volume, set the position of the gauge table on the chart, and toggle the visibility of the volume pressure display. This allows flexibility in integrating the tool with various trading styles and chart layouts. 2. Volume Data Calculations The indicator calculates three key volume metrics: • volToday: The current day’s volume. • volAvg: The average volume over the user-defined SMA period (default is 20 bars). • volPct: The current volume as a percentage of the average. This enables traders to quickly recognize whether current trading activity is above or below normal, which can be a precursor to potential trend strength or weakness. 3. Volume Pressure Calculation The script estimates buying and selling pressure based on price movement and volume. It distributes volume into upward (buy) and downward (sell) segments and expresses them as percentages of the total volume. This gives an immediate sense of whether bulls or bears are more active in the current session. 4. Visual Representation (Progress Bars) The indicator renders a simplified visual gauge using horizontal bar segments (pseudo-bars) to reflect the proportion of buy and sell pressure. The length of each bar correlates with the strength of pressure from buyers or sellers, helping users assess dominance without analyzing candlestick behavior in depth. 5. Table Display A compact table is drawn on the chart showing: • Buy pressure percentage and corresponding bar. • Sell pressure percentage and corresponding bar. • Volume percentage compared to the recent average. This format makes it easy to evaluate volume dynamics at a glance, without cluttering the price chart or relying on separate overlays. ________________________________________ How Traders Benefit from This Indicator • Momentum Shift Detection: Early signs of trend reversal can be observed when volume pressure flips direction. • Breakout Validation: High volume combined with dominant pressure supports the credibility of breakout moves. • False Move Avoidance: If price moves on low volume or mixed pressure, traders can avoid low-probability entries. • Market Context Awareness: Users can assess whether a day is behaving normally in terms of participation or is unusually quiet or aggressive. ________________________________________ Basic Usage Guide 1. Add the script to your TradingView chart and set your preferred SMA length for volume comparison. 2. Customize the table’s position using the X and Y settings for clarity and alignment. 3. Interpret the outputs: o A higher red bar indicates dominant sell pressure. o A higher green bar indicates dominant buy pressure. o Volume % above 100% suggests above-average activity, while values below 100% may imply low conviction. 4. Apply to trading decisions: o High buy pressure and high volume may indicate a strong long opportunity. o High sell pressure and high volume may support short setups. o Low volume or conflicting signals may call for caution. 5. Combine with other tools such as trend indicators, support/resistance zones, or price action patterns for more reliable trade setups. ________________________________________ Practical Example • Sell Pressure: 70% → Suggests strong seller control; potential for short setups. • Buy Pressure: 30% → Weak buying interest; long trades may carry risk. • Volume Percentage: 120% → Indicates a surge in participation; movement may have greater validity. ________________________________________ Tips for New Traders • Use this indicator as a confirmation tool rather than a standalone strategy. • Begin on higher timeframes (4-hour or daily) to develop familiarity. • Compare multiple examples to identify reliable patterns over time. • Always incorporate proper risk management, including stop losses. ________________________________________ Disclaimer from aiTrendview This indicator is intended solely for educational and informational use. It does not constitute investment advice, trade signals, or financial recommendations. aiTrendview and its affiliates are not liable for any trading losses incurred through use of this tool. All trading involves risk. Past performance of any indicator does not guarantee future results. Users should conduct independent research and consult with a certified financial advisor before making any trading decisions.
Wskaźnik Pine Script®
od aiTrendview-Charting-System
1
Game Theory Trading StrategyGame Theory Trading Strategy: Explanation and Working Logic This Pine Script (version 5) code implements a trading strategy named "Game Theory Trading Strategy" in TradingView. Unlike the previous indicator, this is a full-fledged strategy with automated entry/exit rules, risk management, and backtesting capabilities. It uses Game Theory principles to analyze market behavior, focusing on herd behavior, institutional flows, liquidity traps, and Nash equilibrium to generate buy (long) and sell (short) signals. Below, I'll explain the strategy's purpose, working logic, key components, and usage tips in detail. 1. General Description Purpose: The strategy identifies high-probability trading opportunities by combining Game Theory concepts (herd behavior, contrarian signals, Nash equilibrium) with technical analysis (RSI, volume, momentum). It aims to exploit market inefficiencies caused by retail herd behavior, institutional flows, and liquidity traps. The strategy is designed for automated trading with defined risk management (stop-loss/take-profit) and position sizing based on market conditions. Key Features: Herd Behavior Detection: Identifies retail panic buying/selling using RSI and volume spikes. Liquidity Traps: Detects stop-loss hunting zones where price breaks recent highs/lows but reverses. Institutional Flow Analysis: Tracks high-volume institutional activity via Accumulation/Distribution and volume spikes. Nash Equilibrium: Uses statistical price bands to assess whether the market is in equilibrium or deviated (overbought/oversold). Risk Management: Configurable stop-loss (SL) and take-profit (TP) percentages, dynamic position sizing based on Game Theory (minimax principle). Visualization: Displays Nash bands, signals, background colors, and two tables (Game Theory status and backtest results). Backtesting: Tracks performance metrics like win rate, profit factor, max drawdown, and Sharpe ratio. Strategy Settings: Initial capital: $10,000. Pyramiding: Up to 3 positions. Position size: 10% of equity (default_qty_value=10). Configurable inputs for RSI, volume, liquidity, institutional flow, Nash equilibrium, and risk management. Warning: This is a strategy, not just an indicator. It executes trades automatically in TradingView's Strategy Tester. Always backtest thoroughly and use proper risk management before live trading. 2. Working Logic (Step by Step) The strategy processes each bar (candle) to generate signals, manage positions, and update performance metrics. Here's how it works: a. Input Parameters The inputs are grouped for clarity: Herd Behavior (🐑): RSI Period (14): For overbought/oversold detection. Volume MA Period (20): To calculate average volume for spike detection. Herd Threshold (2.0): Volume multiplier for detecting herd activity. Liquidity Analysis (💧): Liquidity Lookback (50): Bars to check for recent highs/lows. Liquidity Sensitivity (1.5): Volume multiplier for trap detection. Institutional Flow (🏦): Institutional Volume Multiplier (2.5): For detecting large volume spikes. Institutional MA Period (21): For Accumulation/Distribution smoothing. Nash Equilibrium (⚖️): Nash Period (100): For calculating price mean and standard deviation. Nash Deviation (0.02): Multiplier for equilibrium bands. Risk Management (🛡️): Use Stop-Loss (true): Enables SL at 2% below/above entry price. Use Take-Profit (true): Enables TP at 5% above/below entry price. b. Herd Behavior Detection RSI (14): Checks for extreme conditions: Overbought: RSI > 70 (potential herd buying). Oversold: RSI < 30 (potential herd selling). Volume Spike: Volume > SMA(20) x 2.0 (herd_threshold). Momentum: Price change over 10 bars (close - close ) compared to its SMA(20). Herd Signals: Herd Buying: RSI > 70 + volume spike + positive momentum = Retail buying frenzy (red background). Herd Selling: RSI < 30 + volume spike + negative momentum = Retail selling panic (green background). c. Liquidity Trap Detection Recent Highs/Lows: Calculated over 50 bars (liquidity_lookback). Psychological Levels: Nearest round numbers (e.g., $100, $110) as potential stop-loss zones. Trap Conditions: Up Trap: Price breaks recent high, closes below it, with a volume spike (volume > SMA x 1.5). Down Trap: Price breaks recent low, closes above it, with a volume spike. Visualization: Traps are marked with small red/green crosses above/below bars. d. Institutional Flow Analysis Volume Check: Volume > SMA(20) x 2.5 (inst_volume_mult) = Institutional activity. Accumulation/Distribution (AD): Formula: ((close - low) - (high - close)) / (high - low) * volume, cumulated over time. Smoothed with SMA(21) (inst_ma_length). Accumulation: AD > MA + high volume = Institutions buying. Distribution: AD < MA + high volume = Institutions selling. Smart Money Index: (close - open) / (high - low) * volume, smoothed with SMA(20). Positive = Smart money buying. e. Nash Equilibrium Calculation: Price mean: SMA(100) (nash_period). Standard deviation: stdev(100). Upper Nash: Mean + StdDev x 0.02 (nash_deviation). Lower Nash: Mean - StdDev x 0.02. Conditions: Near Equilibrium: Price between upper and lower Nash bands (stable market). Above Nash: Price > upper band (overbought, sell potential). Below Nash: Price < lower band (oversold, buy potential). Visualization: Orange line (mean), red/green lines (upper/lower bands). f. Game Theory Signals The strategy generates three types of signals, combined into long/short triggers: Contrarian Signals: Buy: Herd selling + (accumulation or down trap) = Go against retail panic. Sell: Herd buying + (distribution or up trap). Momentum Signals: Buy: Below Nash + positive smart money + no herd buying. Sell: Above Nash + negative smart money + no herd selling. Nash Reversion Signals: Buy: Below Nash + rising close (close > close ) + volume > MA. Sell: Above Nash + falling close + volume > MA. Final Signals: Long Signal: Contrarian buy OR momentum buy OR Nash reversion buy. Short Signal: Contrarian sell OR momentum sell OR Nash reversion sell. g. Position Management Position Sizing (Minimax Principle): Default: 1.0 (10% of equity). In Nash equilibrium: Reduced to 0.5 (conservative). During institutional volume: Increased to 1.5 (aggressive). Entries: Long: If long_signal is true and no existing long position (strategy.position_size <= 0). Short: If short_signal is true and no existing short position (strategy.position_size >= 0). Exits: Stop-Loss: If use_sl=true, set at 2% below/above entry price. Take-Profit: If use_tp=true, set at 5% above/below entry price. Pyramiding: Up to 3 concurrent positions allowed. h. Visualization Nash Bands: Orange (mean), red (upper), green (lower). Background Colors: Herd buying: Red (90% transparency). Herd selling: Green. Institutional volume: Blue. Signals: Contrarian buy/sell: Green/red triangles below/above bars. Liquidity traps: Red/green crosses above/below bars. Tables: Game Theory Table (Top-Right): Herd Behavior: Buying frenzy, selling panic, or normal. Institutional Flow: Accumulation, distribution, or neutral. Nash Equilibrium: In equilibrium, above, or below. Liquidity Status: Trap detected or safe. Position Suggestion: Long (green), Short (red), or Wait (gray). Backtest Table (Bottom-Right): Total Trades: Number of closed trades. Win Rate: Percentage of winning trades. Net Profit/Loss: In USD, colored green/red. Profit Factor: Gross profit / gross loss. Max Drawdown: Peak-to-trough equity drop (%). Win/Loss Trades: Number of winning/losing trades. Risk/Reward Ratio: Simplified Sharpe ratio (returns / drawdown). Avg Win/Loss Ratio: Average win per trade / average loss per trade. Last Update: Current time. i. Backtesting Metrics Tracks: Total trades, winning/losing trades. Win rate (%). Net profit ($). Profit factor (gross profit / gross loss). Max drawdown (%). Simplified Sharpe ratio (returns / drawdown). Average win/loss ratio. Updates metrics on each closed trade. Displays a label on the last bar with backtest period, total trades, win rate, and net profit. j. Alerts No explicit alertconditions defined, but you can add them for long_signal and short_signal (e.g., alertcondition(long_signal, "GT Long Entry", "Long Signal Detected!")). Use TradingView's alert system with Strategy Tester outputs. 3. Usage Tips Timeframe: Best for H1-D1 timeframes. Shorter frames (M1-M15) may produce noisy signals. Settings: Risk Management: Adjust sl_percent (e.g., 1% for volatile markets) and tp_percent (e.g., 3% for scalping). Herd Threshold: Increase to 2.5 for stricter herd detection in choppy markets. Liquidity Lookback: Reduce to 20 for faster markets (e.g., crypto). Nash Period: Increase to 200 for longer-term analysis. Backtesting: Use TradingView's Strategy Tester to evaluate performance. Check win rate (>50%), profit factor (>1.5), and max drawdown (<20%) for viability. Test on different assets/timeframes to ensure robustness. Live Trading: Start with a demo account. Combine with other indicators (e.g., EMAs, support/resistance) for confirmation. Monitor liquidity traps and institutional flow for context. Risk Management: Always use SL/TP to limit losses. Adjust position_size for risk tolerance (e.g., 5% of equity for conservative trading). Avoid over-leveraging (pyramiding=3 can amplify risk). Troubleshooting: If no trades are executed, check signal conditions (e.g., lower herd_threshold or liquidity_sensitivity). Ensure sufficient historical data for Nash and liquidity calculations. If tables overlap, adjust position.top_right/bottom_right coordinates. 4. Key Differences from the Previous Indicator Indicator vs. Strategy: The previous code was an indicator (VP + Game Theory Integrated Strategy) focused on visualization and alerts. This is a strategy with automated entries/exits and backtesting. Volume Profile: Absent in this strategy, making it lighter but less focused on high-volume zones. Wick Analysis: Not included here, unlike the previous indicator's heavy reliance on wick patterns. Backtesting: This strategy includes detailed performance metrics and a backtest table, absent in the indicator. Simpler Signals: Focuses on Game Theory signals (contrarian, momentum, Nash reversion) without the "Power/Ultra Power" hierarchy. Risk Management: Explicit SL/TP and dynamic position sizing, not present in the indicator. 5. Conclusion The "Game Theory Trading Strategy" is a sophisticated system leveraging herd behavior, institutional flows, liquidity traps, and Nash equilibrium to trade market inefficiencies. It’s designed for traders who understand Game Theory principles and want automated execution with robust risk management. However, it requires thorough backtesting and parameter optimization for specific markets (e.g., forex, crypto, stocks). The backtest table and visual aids make it easy to monitor performance, but always combine with other analysis tools and proper capital management. If you need help with backtesting, adding alerts, or optimizing parameters, let me know!
Strategia Pine Script®
od EfeErdost
Mig Trade Model - Kill Zones Key features: Liquidity Hunt Detection: Spots aggressive moves that "hunt" stops beyond recent swing highs/lows. Consolidation Filter: Requires 1-3 small-range candles after a hunt before confirming with a strong candle. Bias Application: Uses daily open/close to auto-detect bias or allows manual override. Kill Zone Restriction: Limits signals to London (default: 7-10 AM UTC) and NY (default: 12-3 PM UTC) sessions for better relevance in active markets. This strategy is inspired by smart money concepts (SMC) and ICT (Inner Circle Trader) methodologies, aiming to capture venom-like "stings" in price action where liquidity is grabbed before reversals. How It Works ATR Calculation: Uses a user-defined ATR length (default: 14) to measure volatility, which scales candle body and range thresholds. Bias Determination: Auto: Compares daily close to open (bullish if close > open). Manual: User selects "Bullish" or "Bearish." Strong Candles: Bullish: Green candle with body > 2x ATR (configurable). Bearish: Red candle with body > 2x ATR. Small Range Candles: Candles where high-low < 0.5x ATR (configurable). Liquidity Hunt: Bullish Hunt: Strong bearish candle making a new low below the past swing low (default: 10 bars). Bearish Hunt: Strong bullish candle making a new high above the past swing high. Signal Generation: After a hunt, counts 1-3 small-range candles. Confirms with a strong candle in the opposite direction (e.g., strong bullish after bearish hunt). Resets if >3 small candles or an opposing strong candle appears. Kill Zone Filter: Checks if the current bar's time (in UTC) falls within London or NY Kill Zones. Only allows final "Buy" (bullish entry) or "Sell" (bearish entry) if bias matches and in Kill Zone. Plots: Yellow circle (below): Bullish liquidity hunt. Orange circle (above): Bearish liquidity hunt. Blue diamond (below): Raw bullish signal. Purple diamond (above): Raw bearish signal. Green triangle up ("Buy"): Filtered bullish entry. Red triangle down ("Sell"): Filtered bearish entry. Inputs Bias: "Auto" (default), "Bullish", or "Bearish" – Controls signal direction based on daily trend. ATR Length: 14 (default) – Period for ATR calculation. Swing Length for Liquidity Hunt: 10 (default) – Bars to look back for swing highs/lows. Strong Candle Body Multiplier (x ATR): 2.0 (default) – Threshold for strong candle bodies. Small Range Multiplier (x ATR): 0.5 (default) – Threshold for small-range candles. London Kill Zone Start/End Hour (UTC): 7/10 (default) – Customize London session hours. NY Kill Zone Start/End Hour (UTC): 12/15 (default) – Customize New York session hours. Usage Tips Timeframe: Best on lower timeframes (e.g., 5-15 min) for intraday trading, especially forex pairs like EURUSD or GBPUSD. Timezone Adjustment: Inputs are in UTC. If your chart is in a different timezone (e.g., EST = UTC-5), adjust hours accordingly (e.g., London: 2-5 AM EST → 7-10 UTC). Risk Management: Use with stop-loss (e.g., beyond the hunt low/high) and take-profit based on ATR multiples. Not financial advice—backtest thoroughly. Customization: Tweak multipliers for different assets; higher for volatile cryptos, lower for stocks. Limitations: Relies on historical data; may generate false signals in ranging markets. Combine with other indicators like volume or support/resistance. This indicator is for educational purposes. Always use discretion and proper risk management in live trading. If you find it useful, feel free to share feedback or suggestions!
Wskaźnik Pine Script®
od EfeErdost
2