Black Friday%80 kadar İNDİRİM

Göstergeler ve stratejiler

BTC Risk Metric DCA Adapter (3Commas Webhook Strategy)Risk Metric DCA Adapter (3Commas Webhook Strategy) - WORK IN PROGRESS This Pine Script strategy, originally inspired by the Risk Metric Indicator, is fundamentally engineered as an Adapter to interface with external trading bots like 3Commas via Webhooks. It calculates a dynamic market risk score and translates that score into specific dollar-cost averaging (DCA) entry levels and tiered profit-taking exits. Key Features & Logic Risk Metric Calculation (Credit to The Trading Parrot): The strategy incorporates a complex, multi-timeframe Risk Metric calculation based on daily and weekly moving averages (SMA) and standard deviation (StDev). This metric aims to quantify the current market overextension or compression relative to long-term historical data. The resulting score dictates the level of conviction for a new trade. Tiered DCA Entry Sizing: The strategy defines three distinct Buy Levels (L1, L2, L3) corresponding to increasingly favorable (lower) Risk Metric scores. L1 (Base): Risk is moderate, initiating the minimum defined trade amount. L2 (Scaled): Risk is low, initiating L1 amount + L2 amount. L3 (Aggressive): Risk is very low, initiating L1 + L2 + L3 amounts. Tiered Profit-Taking Exits: The strategy implements a staggered, partial profit-taking approach based on the Risk Metric rising: Sell L1 & L2: Closes a percentage of the current position when the Risk Metric reaches defined high thresholds, locking in partial profits. Sell L3 (Full Exit): Closes the remaining position when the Risk Metric reaches the highest defined threshold. The Adapter Function (Webhook Integration) This script is unique because it uses the Pine Script strategy() function to trigger Order Fills, which are necessary to access powerful placeholders in the TradingView alert system. Trigger Type: The alert must be set to trigger on Any order fill. Dynamic Webhook Data: Instead of using fixed alert() commands, the strategy generates dynamic labels (e.g., BUY_ENTRY_L3_USD_1000 or SELL_L1_PCT_25) using the strategy.entry and strategy.close commands. Data Transfer: The alert message then uses the placeholder {{strategy.order.comment}} to pass these dynamic labels to the 3Commas bot, allowing the bot to execute the precise action (e.g., start_deal_with_volume_in_quote_currency or close_deal_at_market_percentage). Full Strategy Webhook payload { "secret": "YOUR_3COMMAS_SECRET_KEY", "max_lag": "300", "timestamp": "{{timenow}}", "trigger_price": "{{close}}", "tv_exchange": "{{exchange}}", "tv_instrument": "{{ticker}}", "action": "{{strategy.order.action}}", "bot_uuid": "YOUR_BOT_UUID", "strategy_info": { "market_position": "{{strategy.market_position}}", "market_position_size": "{{strategy.market_position_size}}", "prev_market_position": "{{strategy.prev_market_position}}", "prev_market_position_size": "{{strategy.prev_market_position_size}}" }, "order": { "amount": "{{strategy.order.contracts}}", "currency_type": "base", "comment": "{{strategy.order.comment}}" } } Disclaimer: This script is an adapter tool and does not guarantee profit. Trading requires manual configuration of risk settings, bot parameters, and adherence to platform-specific setup instructions.
Pine Script® stratejisi
nakphanan tarafından
Systemic Net Liquidity (Macro Fuel for Crypto & Stocks)This indicator tracks Systemic Net Liquidity, the single most important macro factor for determining the long-term trend of risk assets like Bitcoin (BTC) and major indices (S&P 500). It measures the amount of actual cash available in the financial system to chase speculative assets, distinguishing between money that is circulating and money that is locked up at the Federal Reserve. ​Mechanism (What It Measures) ​The script uses direct data from the FRED (Federal Reserve Economic Data) to calculate the true state of market funding: \text{Net Liquidity} = \text{Fed Assets (WALCL)} - \text{Treasury General Account (TGA)} - \text{Reverse Repo (RRP)} 1. Fed Assets (WALCL): The total balance sheet of the Fed (The overall supply of money). ​2. Treasury General Account (TGA): Funds the US Treasury collects via bond issuance. When the TGA rises, liquidity is actively drained from the banking system (A major bearish pressure). ​3. Overnight Reverse Repo (RRP): Cash parked by banks and money market funds at the Fed, effectively frozen and not contributing to market activity. ​How to Interpret Signals ​Treat the Net Liquidity line as the market's "Fuel Gauge": ​📈 BULLISH SIGNAL (Liquidity Injection): When the Net Liquidity line is rising, money is flowing back into the system, signalling a tailwind for risk assets. ​📉 BEARISH SIGNAL (Liquidity Drain): When the line is falling (often due to high TGA balances), cash is being removed. This signals major friction and pressure on price action. ​⚠️ DIVERGENCE WARNING: A strong signal is generated when Price (e.g., BTC) rises, but Net Liquidity falls. This macro divergence strongly suggests a major trend reversal or correction is imminent. ​Important Notes ​Data Source: Data is directly sourced from FRED and updates daily/weekly. This tool is best used for macro analysis and identifying high-level cycles, not short-term scalping. ​Disclaimer: Use this indicator as a confirmation tool within your broader strategy. It is not a standalone trading signal.
Pine Script® göstergesi
PlanCco tarafından
Systemic Net Liquidity (Macro Fuel for Crypto & Stocks)This indicator tracks Systemic Net Liquidity, the single most important macro factor for determining the long-term trend of risk assets like Bitcoin (BTC) and major indices (S&P 500). It measures the amount of actual cash available in the financial system to chase speculative assets, distinguishing between money that is circulating and money that is locked up at the Federal Reserve. ​Mechanism (What It Measures) ​The script uses direct data from the FRED (Federal Reserve Economic Data) to calculate the true state of market funding: \text{Net Liquidity} = \text{Fed Assets (WALCL)} - \text{Treasury General Account (TGA)} - \text{Reverse Repo (RRP)} 1. Fed Assets (WALCL): The total balance sheet of the Fed (The overall supply of money). ​2. Treasury General Account (TGA): Funds the US Treasury collects via bond issuance. When the TGA rises, liquidity is actively drained from the banking system (A major bearish pressure). ​3. Overnight Reverse Repo (RRP): Cash parked by banks and money market funds at the Fed, effectively frozen and not contributing to market activity. ​How to Interpret Signals ​Treat the Net Liquidity line as the market's "Fuel Gauge": ​📈 BULLISH SIGNAL (Liquidity Injection): When the Net Liquidity line is rising, money is flowing back into the system, signalling a tailwind for risk assets. ​📉 BEARISH SIGNAL (Liquidity Drain): When the line is falling (often due to high TGA balances), cash is being removed. This signals major friction and pressure on price action. ​⚠️ DIVERGENCE WARNING: A strong signal is generated when Price (e.g., BTC) rises, but Net Liquidity falls. This macro divergence strongly suggests a major trend reversal or correction is imminent. ​Important Notes ​Data Source: Data is directly sourced from FRED and updates daily/weekly. This tool is best used for macro analysis and identifying high-level cycles, not short-term scalping. ​Disclaimer: Use this indicator as a confirmation tool within your broader strategy. It is not a standalone trading signal.
Pine Script® göstergesi
PlanCco tarafından
GBPUSD Weekly Cross LinesThis indicator tracks 20/50 EMA crossovers on GBPUSD (Weekly timeframe) and displays the crossover points across all symbols and timeframes, allowing traders to visually align current price action with key historical turning points in GBPUSD. The script works by detecting bullish (20 EMA crossing above 50 EMA) and bearish (20 EMA crossing below 50 EMA) signals since 2010, using request.security() to source data from GBPUSD weekly candles, even if the indicator is applied to AAPL, EURJPY, BTCUSD, or any other asset. Each crossover is marked with a vertical line that persists across all charts, offering a powerful way to: Compare current market context with GBPUSD’s historical trend shifts Observe intermarket correlations Align trading timing across multiple assets Spot macro trend transitions that ripple across global markets
Pine Script® göstergesi
bellotifang tarafından
3 Fib Strategy – Automatic Trend Fib Extension ConfluenceWhat This Script Does ✔ Auto-detects swing highs and lows Using pivot detection, adjustable by the user. ✔ Builds 3 independent trend-based Fib extension projections Measures: Wave 1 → Wave 2 → Wave 3 Wave 2 → Wave 3 → Wave 4 Wave 3 → Wave 4 → Wave 5 ✔ Calculates the exact fib levels: 1.0 (1:1 extension) 1.236 extension 1.382 extension ✔ Detects confluence zones When all 3 fib measurement sets overlap at the same target: Green label = 1:1 confluence Orange label = 1.236–1.382 confluence ✔ Draws long dotted lines across the chart So you can visually track confluence zones.
Pine Script® göstergesi
ApedinCrypto tarafından
Wavelet Alligator – Separate Entry/Exit Experts & Wavelets-V2 Wavelet Alligator – Strategy Explanation & How to Use 1. Concept Overview The Wavelet Alligator strategy combines: - Wavelet transforms (Daubechies, Haar, Symlet, Mexican Hat, Morlet) - Fractional calculus kernels: Caputo-Fabrizio (CF) and Atangana-Baleanu (AB) - Three-layer “alligator-like” wavelet smoothing (soft → medium → strong) - Expert-based entry/exit routing (RAW, CF, AB, or Majority vote) - Independent wavelets for ENTRY and EXIT - Main trend defined by AB wavelet ordering This creates a multi-structure, multi-kernel trend engine capable of capturing extended moves with high signal quality. 2. Wavelet Alligator Structure Each source (RAW, CF, AB) is transformed into three wavelet layers: Soft = fastest reaction Medium = mid smoothing Strong = trend backbone Wavelets: - Daubechies: stable trend - Haar: fast impulse detection - Symlet: balanced - Mexican Hat: curvature and reversal detection - Morlet: cyclic, oscillatory 3. Entry Logic Long entry occurs when: - AB wavelet shows bullish structure (soft > medium > strong, medium rising) - Selected entry expert approves (RAW / CF / AB / Majority) - Wavelet condition: soft > strong AND medium crosses above strong 4. Exit Logic Exit is independent from entry: - Controlled by chosen exit expert - Wavelet reversal condition: soft < strong AND medium crosses below strong - Forced exit when AB trend turns neutral or bearish 5. Background Color (Regime) - Green: bullish AB regime - Red: bearish AB regime - Gray: neutral/transition 6. How to Use Step 1 – Choose entry wavelet Daubechies: stable trend Haar: breakout scalping Mexican Hat: early reversals Symlet: balanced Morlet: cyclic markets Step 2 – Choose exit wavelet Mexican Hat: best precision Daubechies: smooth exits Haar: aggressive exits Step 3 – Select entry/exit experts CF only – fast fractional trend AB only – stable long-memory trend RAW only – pure price structure Majority – safest, noise-filtered Step 4 – Run the strategy Entries occur only during AB bullish trend. Exits occur on wavelet reversal or AB trend failure. 7. Why This Strategy Works It fuses: - Fractional calculus (memory) - Wavelets (shape/curvature) - Alligator ordering (trend hierarchy) Result: high-quality entries, strong trend holding, noise-resistant signals.
Pine Script® stratejisi
wojlucz tarafından
Uptrick: Dynamic Z-Score DivergenceIntroduction Uptrick: Dynamic Z-Score Divergence is an oscillator that combines multiple momentum sources within a Z-Score framework, allowing for the detection of statistically significant mean-reversion setups, directional shifts, and divergence signals. It integrates a multi-source normalized oscillator, a slope-based signal engine, structured divergence logic, a slope-adaptive EMA with dynamic bands, and a modular bar coloring system. This script is designed to help traders identify statistically stretched conditions, evolving trend dynamics, and classical divergence behavior using a unified statistical approach. Overview At its core, this script calculates the Z-Score of three momentum sources—RSI, Stochastic RSI, and MACD—using a user-defined lookback period. These are averaged and smoothed to form the main oscillator line. This normalized oscillator reflects how far short-term momentum deviates from its mean, highlighting statistically extreme areas. Signals are triggered when the oscillator reverses slope within defined inner zones, indicating a shift in direction while the signal remains in a statistically stretched state. These mean-reversion flips (referred to as TP signals) help identify turning points when price momentum begins to revert from extended zones. In addition, the script includes a divergence detection engine that compares oscillator pivot points with price pivot points. It confirms regular bullish and bearish divergence by validating spacing between pivots and visualizes both the oscillator-side and chart-side divergences clearly. A dynamic trend overlay system is included using a Slope Adaptive EMA (SA-EMA). This trend line becomes more responsive when Z-Score deviation increases, allowing the trend line to adapt to market conditions. It is paired with ATR-based bands that are slope-sensitive and selectively visible—offering context for dynamic support and resistance. The script includes configurable bar coloring logic, allowing users to color candles based on oscillator slope, last confirmed divergence, or the most recent signal of any type. A full alert system is also built-in for key signals. Originality The script is based on the well-known concept of Z-Score valuation, which is a standard statistical method for identifying how far a signal deviates from its mean. This foundation—normalizing momentum values such as RSI or MACD to measure relative strength or weakness—is not unique to this script and is widely used in quantitative analysis. What makes this implementation original is how it expands the Z-Score foundation into a fully featured, signal-producing system. First, it introduces a multi-source composite oscillator by combining three momentum inputs—RSI, Stochastic RSI, and MACD—into a unified Z-Score stream. Second, it builds on that stream with a directional slope logic that identifies turning points inside statistical zones. The most distinctive additions are the layered features placed on top of this normalized oscillator: A structured divergence detection engine that compares oscillator pivots with price pivots to validate regular bullish and bearish divergence using precise spacing and timing filters. A fully integrated slope-adaptive EMA overlay, where the smoothing dynamically adjusts based on real-time Z-Score movement of RSI, allowing the trend line to become more reactive during high-momentum environments and slower during consolidation. ATR-based dynamic bands that adapt to slope direction and offer real-time visual zones for support and resistance within trend structures. These features are not typically found in standard Z-Score indicators and collectively provide a unique approach that bridges statistical normalization, structure detection, and adaptive trend modeling within one script. Features Z-Score-based oscillator combining RSI, StochRSI, and MACD Configurable smoothing for stable composite signal output Buy/Sell TP signals based on slope flips in defined zones Background highlighting for extreme outer bands Inner and outer zones with fill logic for statistical context Pivot-based divergence detection (regular bullish/bearish) Divergence markers on oscillator and price chart Slope-Adaptive EMA (SA-EMA) with real-time adaptivity based on RSI Z-Score ATR-based upper and lower bands around the SA-EMA, visibility tied to slope direction Configurable bar coloring (oscillator slope, divergence, or most recent signal) Alerts for TP signals and confirmed divergences Optional fixed Y-axis scaling for consistent oscillator view The full setup mode can be seen below: Input Parameters General Settings Full Setup: Enables rendering of the full visual system (lines, bands, signals) Z-Score Lookback: Lookback period for normalization (mean and standard deviation) Main Line Smoothing: EMA length applied to the averaged Z-Score Slope Detection Index: Used to calculate directional flips for signal logic Enable Background Highlighting: Enables visual region coloring in overbought/oversold areas Force Visible Y-Axis Scale: Forces max/min bounds for a consistent oscillator range Divergence Settings Enable Divergence Detection: Toggles divergence logic Pivot Lookback Left / Right: Defines the structure of oscillator pivot points Minimum / Maximum Bars Between Pivots: Controls the allowed spacing range for divergence validation Bar Coloring Settings Bar Coloring Mode: ➜ Line Color: Colors bars based on oscillator slope ➜ Latest Confirmed Signal: Colors bars based on the most recent confirmed divergence ➜ Any Latest Signal: Colors based on the most recent signal (TP or divergence) SA-EMA Settings RSI Length: RSI period used to determine adaptivity Z-Score Length: Lookback for normalizing RSI in adaptive logic Base EMA Length: Base length for smoothing before adaptivity Adaptivity Intensity: Scales the smoothing responsiveness based on RSI deviation Slope Index: Determines slope direction for coloring and band logic Band ATR Length / Band Multiplier: Controls the width and responsiveness of the trend-following bands Alerts The script includes the following alert conditions: Buy Signal (TP reversal detected in oversold zone) Sell Signal (TP reversal detected in overbought zone) Confirmed Bullish Divergence (oscillator HL, price LL) Confirmed Bearish Divergence (oscillator LH, price HH) These alerts allow integration into automation systems or signal monitoring setups. Summary Uptrick: Dynamic Z-Score Divergence is a statistically grounded trading indicator that merges normalized multi-momentum analysis with real-time slope logic, divergence detection, and adaptive trend overlays. It helps traders identify mean-reversion conditions, divergence structures, and evolving trend zones using a modular system of statistical and structural tools. Its alert system, layered visuals, and flexible input design make it suitable for discretionary traders seeking to combine quantitative momentum logic with structural pattern recognition. Disclaimer This script is for educational and informational purposes only. No indicator can guarantee future performance, and trading involves risk. Always use risk management and test strategies in a simulated environment before deploying with live capital.
Pine Script® göstergesi
Uptrick tarafından
7
Pair Cointegration & Static Beta Analyzer (v6)Pair Cointegration & Static Beta Analyzer (v6) This indicator evaluates whether two instruments exhibit statistical properties consistent with cointegration and tradable mean reversion. It uses long-term beta estimation, spread standardization, AR(1) dynamics, drift stability, tail distribution analysis, and a multi-factor scoring model. 1. Static Beta and Spread Construction A long-horizon static beta is estimated using covariance and variance of log-returns. This beta does not update on every bar and is used throughout the entire model. Beta = Cov(r1, r2) / Var(r2) Spread = PriceA - Beta * PriceB This “frozen” beta provides structural stability and avoids rolling noise in spread construction. 2. Correlation Check Log-price correlation ensures the instruments move together over time. Correlation ≥ 0.85 is required before deeper cointegration diagnostics are considered meaningful. 3. Z-Score Normalization and Distribution Behavior The spread is standardized: Z = (Spread - MA(Spread)) / Std(Spread) The following statistical properties are examined: Z-Mean: Should be close to zero in a stationary process Z-Variance: Measures amplitude of deviations Tail Probability: Frequency of |Z| being larger than a threshold (e.g. 2) These metrics reveal whether the spread behaves like a mean-reverting equilibrium. 4. Mean Drift Stability A rolling mean of the spread is examined. If the rolling mean drifts excessively, the spread may not represent a stable long-term equilibrium. A normalized drift ratio is used: Mean Drift Ratio = Range( RollingMean(Spread) ) / Std(Spread) Low drift indicates stable long-run equilibrium behavior. 5. AR(1) Dynamics and Half-Life An AR(1) model approximates mean reversion: Spread(t) = Phi * Spread(t-1) + error Mean reversion requires: 0 < Phi < 1 Half-life of reversion: Half-life = -ln(2) / ln(Phi) Valid half-life for 10-minute bars typically falls between 3 and 80 bars. 6. Composite Scoring Model (0–100) A multi-factor weighted scoring system is applied: Component Score Correlation 0–20 Z-Mean 0–15 Z-Variance 0–10 Tail Probability 0–10 Mean Drift 0–15 AR(1) Phi 0–15 Half-Life 0–15 Score interpretation: 70–100: Strong Cointegration Quality 40–70: Moderate 0–40: Weak A pair is classified as cointegrated when: Total Score ≥ Threshold (default = 70) 7. Main Cointegration Panel Displays: Static beta Log-price correlation Z-Mean, Z-Variance, Tail Probability Drift Ratio AR(1) Phi and Half-life Composite score Overall cointegration assessment 8. Beta Hedge Position Sizing (Average-Price Based) To provide a more stable hedge ratio, hedge sizing is computed using average prices, not instantaneous prices: AvgPriceA = SMA(PriceA, N) AvgPriceB = SMA(PriceB, N) Required B per 1 A = Beta * (AvgPriceA / AvgPriceB) Using averaged prices results in a smoother, more reliable hedge ratio, reducing noise from bar-to-bar volatility. The panel displays: Required B security for 1 A security (average) This represents the beta-neutral quantity of B required to hedge one unit of A. Overview of Classical Stationarity & Cointegration Methods The principal econometric tools commonly used in assessing stationarity and cointegration include: Augmented Dickey–Fuller (ADF) Test Phillips–Perron (PP) Test KPSS Test Engle–Granger Cointegration Test Phillips–Ouliaris Cointegration Test Johansen Cointegration Test Since these procedures rely on regression residuals, matrix operations, and distribution-based critical values that are not supported in TradingView Pine Script, a practical multi-criteria scoring approach is employed instead. This framework leverages metrics that are fully computable in Pine and offers an operational proxy for evaluating cointegration-like behavior under platform constraints. References Engle & Granger (1987), Co-integration and Error Correction Poterba & Summers (1988), Mean Reversion in Stock Prices Vidyamurthy (2004), Pairs Trading Explanation structured with assistance from OpenAI’s ChatGPT Regards.
Pine Script® göstergesi
Noldo tarafından