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Mark4ex vWapMark4ex VWAP is a precision session-anchored Volume Weighted Average Price (VWAP) indicator crafted for intraday traders who want clean, reliable VWAP levels that reset daily to match a specific market session. Unlike the built-in continuous VWAP, this version anchors each day to your chosen session start and end time, most commonly aligned with the New York Stock Exchange Open (9:30 AM EST) through the market close (4:00 PM EST). This ensures your VWAP reflects only intraday price action within your active trading window — filtering out irrelevant overnight moves and providing clearer mean-reversion signals. Key Features: Fully configurable session start & end times — adapt it for NY session or any other market. Anchored VWAP resets daily for true session-based levels. Built for the New York Open Range Breakout strategy: see how price interacts with VWAP during the volatile first 30–60 minutes of the US market. Plots a clean, dynamic line that updates tick-by-tick during the session and disappears outside trading hours. Designed to help you spot real-time support/resistance, intraday fair value zones, and liquidity magnets used by institutional traders. How to Use — NY Open Range Breakout: During the first hour of the New York session, institutional traders often define an “Opening Range” — the high and low formed shortly after the bell. The VWAP in this zone acts as a dynamic pivot point: When price is above the session VWAP, bulls are in control — the level acts as a support floor for pullbacks. When price is below the session VWAP, bears dominate — the level acts as resistance against bounces. Breakouts from the opening range often test the VWAP for confirmation or rejection. Traders use this to time entries for breakouts, retests, or mean-reversion scalps with greater confidence. ⚙️ Recommended Settings: Default: 9:30 AM to 4:00 PM New York time — standard US equities session. Adjust hours/minutes to match your target market’s open and close. 👤 Who is it for? Scalpers, day traders, prop traders, and anyone trading the NY Open, indices like the S&P 500, or highly liquid stocks during US cash hours. 🚀 Why use Mark4ex VWAP? Because a properly anchored VWAP is a trader’s real-time institutional fair value, giving you better context than static moving averages. It adapts live to volume shifts and helps you follow smart money footprints. This indicator will reconfigure every day, anchored to the New York Open, it will also leave historical NY Open VWAP for study purpose.
Pine Script® indicator
by MarkLeRoy
[alert-custome] direction-ema-dca-rsi-security Below is a detailed description of the Pine Script v5 strategy titled direction-ema-dca-rsi-security. This strategy automates Long or Short trading on cryptocurrency pairs (e.g., BTCUSDT.P), leveraging technical indicators such as EMA, RSI, and ATR, combined with a Dollar Cost Averaging (DCA) approach. It integrates with OKX via alerts for automated trading execution. Overview Strategy Name: direction-ema-dca-rsi-security Objective: Automate Long or Short trades using EMA, RSI, and ATR signals, with DCA to improve average position price when the market moves against the position. The strategy allows customization of trade direction (Long or Short) and integrates with OKX for automated execution. Type: Supports both Long and Short trades, using leverage and flexible capital management. Target Market: Designed for high-volatility cryptocurrency markets, particularly perpetual futures pairs like BTCUSDT.P. Key Features: Uses Fast and Slow EMAs to identify trends and entry points. Combines RSI from the current timeframe and a user-defined timeframe (security RSI) to detect overbought/oversold conditions. Implements DCA to add to positions when prices move unfavorably. Integrates with OKX via JSON alerts for automated trading. Supports Take Profit (TP) and Stop Loss (SL) based on ATR or percentage values. Structure and Key Components 1. Strategy Configuration Basic Settings: Initial Capital: $1,000. Leverage: Default 6x, adjustable from 1x to 10x. Order Type: Cash-based, with a default quantity of 10. Commission: 0.1% per trade. Pyramiding: Allows up to 100 concurrent orders. Slippage: 3 pips. Backtest Fill Limits Assumption: 3 pips, ensuring limit orders are filled within a price range. Calculate on Order Fills: Enabled (calc_on_order_fills = true). Use Bar Magnifier: Enabled for detailed candle data calculations. Fill Orders on Standard OHLC: Enabled, ensuring orders match standard open, high, low, close prices. 2. Input Parameters The strategy offers customizable inputs, grouped as follows: Strategy: Fast/Slow EMA Length: Fast EMA (default 9), Slow EMA (default 21) for trend detection. ATR Length: Default 14 for volatility measurement. RSI Length: Default 14 for overbought/oversold detection. RSI Security Timeframe: Default 15 minutes (options: 5m, 15m, 30m, 1h, 4h, D, W, M). Direction Security Timeframe: Default daily (options: 4h, D, W, M). Strategy Size: Init Webhook Balance ($): Initial balance for webhook (default 0, uses strategy balance if unset). Leverage: Financial leverage (default 6x). Init Size Equity (%): Initial position size as a percentage of equity (default 2%). Size Increase (%): Position size increase per DCA order (default 15%). Max DCA Orders: Maximum DCA orders (default 15). Strategy DCA: Init Percent to DCA (%): Initial price drop/rise for DCA (default 2%). Increase Step Percent to DCA (%): Incremental DCA price adjustment (default 1%). Decrease DCA with ATR: ATR multiplier for DCA price (default 0, disabled). Strategy DCA RSI: RSI to DCA: RSI threshold for DCA (default 50, below for Long, above for Short). Security RSI to DCA: RSI threshold for security timeframe (default 50). Strategy Delay: Off-Time Delays / Order (s): Delay between orders (default 1000 seconds). Off-Time Active Stop Loss (Hour): Delay for activating Stop Loss (default 0, disabled). Strategy TPSL (Take Profit/Stop Loss): ATR Multiplier TP: ATR multiplier for Take Profit (default 2x). Init TP (%): Initial Take Profit percentage (default 2%). ATR Multiplier SL: ATR multiplier for Stop Loss (default 0, disabled). Init SL (%): Initial Stop Loss percentage (default 0%, disabled). Strategy Direction: Trade Direction: Trade direction (default Long, options: Long, Short). Strategy OKX: OKX Signal Key: Signal key for OKX API integration. 3. Market Data Technical Indicators: EMA: Fast EMA (9) and Slow EMA (21) on the current timeframe for trend identification. RSI: RSI (14) on the current timeframe and a user-defined timeframe (rsiSecurityTimeframe) for overbought/oversold signals. ATR: ATR (14) for volatility-based calculations of TP, SL, and DCA prices. External Library: Uses jason5480/chrono_utils/6 for time-related functions. 4. Entry Conditions Long Position: Condition 1: Fast EMA crosses above Slow EMA (ta.crossover(fastMA, slowMA)), or Condition 2: RSI ≤ rsiTrigger (default 50), Fast EMA ≤ Slow EMA, and RSI security ≤ rsiSecurityTrigger (default 50). Short Position: Condition 1: Fast EMA crosses below Slow EMA (ta.crossunder(fastMA, slowMA)), or Condition 2: RSI ≥ rsiTrigger, Fast EMA ≥ Slow EMA, and RSI security ≥ rsiSecurityTrigger. Constraints: Entries are allowed only if tradeDirection matches the signal (Long or Short). Open trades ≤ maxDCAOrders (default 15). Current price meets DCA conditions (if positions exist). Time since last order ≥ offTimeMsOpen (default 1000 seconds). 5. Dollar Cost Averaging (DCA) DCA Conditions: Long: Current price ≤ DCA price, calculated as: strategy.position_avg_price - (atrValue * decreaseDCAWithATR), or strategy.position_avg_price * (1 - currentUnderPercentDCA) (default 2%, increasing by 1% per DCA). Short: Current price ≥ DCA price, calculated similarly but for price increases. DCA Management: Position size increases by 15% per DCA (stepSizePercent). DCA price distance increases by 1% per order (stepDecreasePercentDCA). Maximum 15 DCA orders (maxDCAOrders). 6. Position Management Position Size: Initial size: 2% of equity (initSizeEquity). Increases by 15% per DCA order. Limited by leverage and current balance (currentBalance). Current Balance: If webhookInitBalance = 0, uses initial_capital + netprofit + openprofit. If webhookInitBalance > 0, uses this value plus net and open profits. Order Delay: Ensures a minimum gap of 1000 seconds between orders (offTimeMsOpen). 7. Exit Conditions Take Profit (TP): Long: strategy.position_avg_price + (atrValue * atrMultiplierTP) (default 2x ATR), or strategy.position_avg_price * (1 + initTP) (default 2%). Short: strategy.position_avg_price - (atrValue * atrMultiplierTP), or strategy.position_avg_price * (1 - initTP). Stop Loss (SL): Long: strategy.position_avg_price - (atrValue * atrMultiplierSL), or strategy.position_avg_price * (1 - initSL). Short: strategy.position_avg_price + (atrValue * atrMultiplierSL), or strategy.position_avg_price * (1 + initSL). Currently, atrMultiplierSL and initSL = 0, meaning Stop Loss is disabled. Exit Execution: Uses strategy.order for Long TP and strategy.exit for Short TP, closing the entire position when TP is reached. Sends OKX alerts for exits (EXIT_LONG or EXIT_SHORT). 8. Visualization Plots: Fast EMA: Red. Slow EMA: Aqua. Take Profit Price: Lime. Position Average Price: Gray. Background Color: Commented out, but can display green for Long or red for Short. 9. OKX Integration Alerts: Sends JSON alerts for entries (ENTER_LONG, ENTER_SHORT) and exits (EXIT_LONG, EXIT_SHORT), including: Market position, size, order type (market), and investment percentage. OKX signal key (okxSignalKeyInput) for API integration. How the Strategy Works Market Analysis: Uses EMA (9, 21) for trend detection (crossover/crossunder). Combines RSI from the current and security timeframes to confirm overbought/oversold conditions. ATR measures volatility for TP, SL, and DCA price calculations. Entry: Long: Triggers on EMA crossover or RSI in oversold territory with a bearish trend. Short: Triggers on EMA crossunder or RSI in overbought territory with a bullish trend. Entries are restricted by tradeDirection setting. DCA: Adds positions when price moves against the trade (down for Long, up for Short) based on RSI or ATR conditions. Increases position size and DCA price distance per order. Exit: Closes positions when price hits TP (ATR or percentage-based). Stop Loss is currently disabled, posing a risk. Risk Management: Limits DCA orders to 15. Enforces time delays between orders. Caps leverage at 10x. Strengths Flexible Direction: Supports both Long and Short trades. Effective DCA: Improves average position price in volatile markets. OKX Integration: Automates trading via OKX API. Multi-Timeframe Analysis: Uses RSI from a secondary timeframe for confirmation. Customizable Capital Management: Adjustable position sizes and leverage. Weaknesses No Stop Loss: Disabled SL increases risk of large losses. OKX Dependency: Requires accurate API configuration. Simple Conditions: Relies primarily on EMA and RSI, potentially lacking depth. Fixed Timeframes: Commented-out EMA security logic limits trend analysis flexibility. Practical Applications Target Market: High-volatility crypto markets, especially perpetual futures like BTCUSDT.P. Trading Style: Suitable for short- to medium-term traders using DCA for risk management. Optimization: Adjust fastMALen, slowMALen, rsiTrigger, or maxDCAOrders for specific markets. Improvement Suggestions Enable Stop Loss: Set atrMultiplierSL or initSL > 0 to protect capital. Use EMA Security: Uncomment fastDirectionEMA and slowDirectionEMA code to enhance trend accuracy. Optimize Parameters: Use TradingView’s optimization tool to fine-tune rsiTrigger, atrMultiplierTP, or maxDCAOrders. Add Indicators: Incorporate Volume, ADX, or other indicators for robust entry signals. Leverage Alerts: Add warnings for high-leverage risks in volatile conditions. If you need further analysis, code optimization, or additional strategy development, let me know!
Pine Script® strategy
by albeydkovenw07
NA GPT - TTM Squeeze Strategy**NA GPT - TTM Squeeze Strategy**” transforms the well-known TTM Squeeze indicator into a back-testable, long-only strategy. It combines **volatility compression** (Bollinger Bands inside Keltner Channels) with **momentum confirmation** to catch powerful bullish breakouts and then trails positions with a simple 21-period moving-average stop. --- ## 1. Core Concepts & Calculations | Component | What it measures | How it works | | ---------------------- | ------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Squeeze State** | Volatility contraction vs. expansion | • Calculate a 20-period Bollinger Band (BB). • Calculate a 20-period Keltner Channel (KC). • **Squeeze ON** when the entire BB is *inside* the KC (narrow volatility). | | **Momentum Histogram** | Direction & strength of pressure building inside the squeeze | • Compute the midpoint of the recent high/low range and the 20-period SMA of close. • Take the price’s deviation from that blended average. • Fit a **20-period linear regression** to that deviation to produce the histogram. • Color logic: ↗ increasing green/lime for strengthening bulls, ↘ red/maroon for bears. | | **Blue-Dot Theme** | Visual cue of squeeze state | • **Navy-blue dot** = Squeeze ON (ready to pop). • **Steel-blue dot** = Squeeze just released. • **Sky-blue dot** = Neutral (no squeeze). | --- ## 2. Trade Logic | Step | Condition | Rationale | | ---------------- | ------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | | **Entry (Long)** | Three consecutive navy-blue dots (i.e., **3 bars with Squeeze ON in a row**) *inside the user-defined date window*. | Requires sustained volatility compression before committing capital, filtering out one-bar “fake” squeezes. | | **Exit (Flat)** | Close price **crosses below** the 21-period Simple Moving Average. | A classic trailing stop that lets profits run while cutting momentum failures. | | **Positioning** | Long-only, one trade at a time. No short sales. | Focuses on bullish breakouts, keeps strategy simple and margin-friendly. | --- ## 3. Inputs & Customisation | Input | Default | Purpose | | -------------------------- | ------------------------ | ---------------------------------------------- | | **BB Length / Multiplier** | 20 / 2.0 | Adjust sensitivity of Bollinger Bands. | | **KC Length / Multiplier** | 20 / 1.5 | Adjust Keltner Channel width. | | **Use True Range (KC)** | ✔ | Pick *True Range* vs. *High-Low* for KC width. | | **Trade Window** | 1 Jan 2022 → 31 Dec 2069 | Back-test only the period you care about. | | **Commission** | 0.01 % | Embedded in `strategy()` header. | | **Slippage (Ticks)** | 3 | Models real-world order fill uncertainty. | --- ## 4. Visual Outputs * **Momentum Histogram** (green / lime / red / maroon). * **Zero Line** colored by squeeze state (blue / dark navy / grey). * **Blue-Dot Row** at the chart bottom showing squeeze timing. The visuals are **identical** to the original indicator, letting you correlate back-test trades with familiar chart cues. --- ## 5. How to Use 1. **Add to chart**, choose your symbol & timeframe (works on anything from 1-minute to weekly). 2. **Tune the BB/KC/ATR settings** to match instrument volatility. 3. **Adjust the date window** for focused walk-forward testing. 4. Run the “**Strategy Tester**” to inspect historical performance, P\&L; curves, drawdowns, and trade list. 5. Use the plotted dots & histogram to visually validate why each trade fired. 6. Combine with your own risk-management (position sizing, portfolio filters, etc.) before going live. --- ## 6. Practical Notes * Designed for educational/back-testing purposes—**not financial advice**. * Long-only by design; add your own short logic if desired. * Because exits rely on the 21-SMA, extremely low-volume instruments or illiquid intraday timeframes may experience wider drawdowns. * Commission/slippage values are easily editable in the first line of the script.
Pine Script® strategy
by n-a-
AQS Gold Strategy//@version=5 indicator("AQS Gold Strategy", overlay=true) // === المؤشرات === // EMA 200 لتحديد الاتجاه ema200 = ta.ema(close, 200) plot(ema200, color=color.orange, title="EMA 200") // MACD = ta.macd(close, 12, 26, 9) macd_cross_up = ta.crossover(macdLine, signalLine) macd_cross_down = ta.crossunder(macdLine, signalLine) // Stochastic RSI k = ta.stoch(close, high, low, 14) d = ta.sma(k, 3) stoch_overbought = k > 80 and d > 80 stoch_oversold = k < 20 and d < 20 // Volume Filter vol_condition = volume > ta.sma(volume, 20) // === شروط الدخول والخروج === // دخول شراء: تقاطع MACD صاعد + تشبع شراء في Stoch RSI + السعر فوق EMA 200 long_condition = macd_cross_up and stoch_oversold and close > ema200 and vol_condition // خروج شراء أو دخول بيع: تقاطع MACD هابط + تشبع بيع في Stoch RSI + السعر تحت EMA 200 short_condition = macd_cross_down and stoch_overbought and close < ema200 and vol_condition // === رسم إشارات الدخول والخروج === plotshape(long_condition, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="BUY") plotshape(short_condition, title="Sell Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SELL") // === تنبيهات === alertcondition(long_condition, title="Buy Alert", message="إشارة شراء حسب استراتيجية AQS Gold") alertcondition(short_condition, title="Sell Alert", message="إشارة بيع حسب استراتيجية AQS Gold")
Pine Script® indicator
by qasimalsharafi0068
Trend ShaderThis simple but useful indicator automatically detects whether you’re on a 1 minute, 5 minute, or 15 minute chart (with sensible, pre-tuned EMA settings), and shades the chart background green when the trend is bullish, or red when it’s bearish. On any other timeframe it falls back to user-specified “manual” EMA lengths. The short and long EMA lengths are configurable on all timeframes. Really useful when you want to trade in the direction of the overall trend. The color doesn't change until the trend is confirmed by a reasonable number of bars in the same direction so it doesn't just flip-flop with market noise. The confirmation bar count for each timeframe is also configurable.
Pine Script® indicator
by The_Collector2
AQS Gold Strategy//@version=5 indicator("AQS Gold Strategy", overlay=true) // === المؤشرات === // EMA 200 لتحديد الاتجاه ema200 = ta.ema(close, 200) plot(ema200, color=color.orange, title="EMA 200") // MACD = ta.macd(close, 12, 26, 9) macd_cross_up = ta.crossover(macdLine, signalLine) macd_cross_down = ta.crossunder(macdLine, signalLine) // Stochastic RSI k = ta.stoch(close, high, low, 14) d = ta.sma(k, 3) stoch_overbought = k > 80 and d > 80 stoch_oversold = k < 20 and d < 20 // Volume Filter vol_condition = volume > ta.sma(volume, 20) // === شروط الدخول والخروج === // دخول شراء: تقاطع MACD صاعد + تشبع شراء في Stoch RSI + السعر فوق EMA 200 long_condition = macd_cross_up and stoch_oversold and close > ema200 and vol_condition // خروج شراء أو دخول بيع: تقاطع MACD هابط + تشبع بيع في Stoch RSI + السعر تحت EMA 200 short_condition = macd_cross_down and stoch_overbought and close < ema200 and vol_condition // === رسم إشارات الدخول والخروج === plotshape(long_condition, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="BUY") plotshape(short_condition, title="Sell Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SELL") // === تنبيهات === alertcondition(long_condition, title="Buy Alert", message="إشارة شراء حسب استراتيجية AQS Gold") alertcondition(short_condition, title="Sell Alert", message="إشارة بيع حسب استراتيجية AQS Gold")
Pine Script® indicator
by qasimalsharafi0068
M2 Growth Rate vs Borrowing RateHave you ever wondered how fast M2 is actually growing? Have you ever wanted to compare its percentage growth rate to the actual cost of borrowing? Are you also, like me, a giant nerd with too much time on your hands? M2 Growth Rate vs Borrowing Rate This Pine Script indicator analyzes the annualized growth rate of M2 money supply and compares it to key borrowing rates, providing insights into the relationship between money supply expansion and borrowing costs. Users can select between US M2 or a combined M2 (aggregating US, EU, China, Japan, and UK money supplies, adjusted for currency exchange rates). The M2 growth period is customizable, offering options from 1 month to 5 years for flexible analysis over different time horizons. The indicator fetches monthly data for US M2, EU M2, China M2, Japan M2, UK M2, and exchange rates (EURUSD, CNYUSD, JPYUSD, GBPUSD) to compute the combined M2 in USD terms. It plots the annualized M2 growth rate alongside borrowing rates, including US 2-year and 10-year Treasury yields, corporate bond effective yield, high-yield bond effective yield, and 30-year US mortgage rates. Borrowing rates are color-coded for clarity: red if the rate exceeds the selected M2 growth rate, and green if below, highlighting relative dynamics. Displayed on a separate pane with a zero line for reference, the indicator includes labeled plots for easy identification. This tool is designed for informational purposes, offering a visual framework to explore economic trends without providing trading signals or financial advice.
Pine Script® indicator
by capital_flight
1
Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis. ## 1. Introduction Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017). The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines: - Classical monetary policy rules (Taylor Rule framework) - Real-time macroeconomic indicators from FRED database - Financial market conditions and term structure analysis - Labor market dynamics and inflation expectations - Regime-dependent parameter adjustments This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes. ## 2. Literature Review and Theoretical Foundation ### 2.1 Taylor Rule Framework The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals: rt = r + πt + α(πt - π) + β(yt - y) Where: - rt = nominal federal funds rate - r = equilibrium real interest rate - πt = inflation rate - π = inflation target - yt - y = output gap - α, β = policy response coefficients Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions. ### 2.2 Data-Dependent Monetary Policy The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions. ### 2.3 Financial Conditions and Monetary Transmission The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010). ### 2.4 Labor Market Indicators The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments. ## 3. Methodology ### 3.1 Model Architecture The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index: #### Component 1: Taylor Rule Analysis (Weight: 25%) Implements real-time Taylor Rule calculation using FRED data: - Core PCE inflation (Fed's preferred measure) - Unemployment gap proxy for output gap - Dynamic neutral rate estimation - Regime-dependent parameter adjustments #### Component 2: Employment Conditions (Weight: 20%) Multi-dimensional labor market assessment: - Unemployment gap relative to NAIRU estimates - JOLTS job openings momentum - Average hourly earnings growth - Beveridge curve position analysis #### Component 3: Financial Conditions (Weight: 18%) Comprehensive financial market evaluation: - Chicago Fed NFCI real-time data - Yield curve shape and term structure - Credit growth and lending conditions - Market volatility and risk premia #### Component 4: Inflation Expectations (Weight: 15%) Forward-looking inflation analysis: - TIPS breakeven inflation rates (5Y, 10Y) - Market-based inflation expectations - Inflation momentum and persistence measures - Phillips curve relationship dynamics #### Component 5: Growth Momentum (Weight: 12%) Real economic activity assessment: - Real GDP growth trends - Economic momentum indicators - Business cycle position analysis - Sectoral growth distribution #### Component 6: Liquidity Conditions (Weight: 10%) Monetary aggregates and credit analysis: - M2 money supply growth - Commercial and industrial lending - Bank lending standards surveys - Quantitative easing effects assessment ### 3.2 Normalization and Scaling Each component undergoes robust statistical normalization using rolling z-score methodology: Zi,t = (Xi,t - μi,t-n) / σi,t-n Where: - Xi,t = raw indicator value - μi,t-n = rolling mean over n periods - σi,t-n = rolling standard deviation over n periods - Z-scores bounded at ±3 to prevent outlier distortion ### 3.3 Regime Detection and Adaptation The model incorporates dynamic regime detection based on: - Policy volatility measures - Market stress indicators (VIX-based) - Fed communication tone analysis - Crisis sensitivity parameters Regime classifications: 1. Crisis: Emergency policy measures likely 2. Tightening: Restrictive monetary policy cycle 3. Easing: Accommodative monetary policy cycle 4. Neutral: Stable policy maintenance ### 3.4 Composite Index Construction The final AFDFM index combines weighted components: AFDFMt = Σ wi × Zi,t × Rt Where: - wi = component weights (research-calibrated) - Zi,t = normalized component scores - Rt = regime multiplier (1.0-1.5) Index scaled to range for intuitive interpretation. ### 3.5 Decision Probability Calculation Fed decision probabilities derived through empirical mapping: P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100) P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100) P(Hold) = 100 - |AFDFMt| × 15 Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds). ## 4. Data Sources and Real-Time Implementation ### 4.1 FRED Database Integration - Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted - Unemployment Rate (UNRATE): Monthly, seasonally adjusted - Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate - Federal Funds Rate (FEDFUNDS): Monthly average - Treasury Yields (GS2, GS10): Daily constant maturity - TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data ### 4.2 High-Frequency Financial Data - Chicago Fed NFCI: Weekly financial conditions - JOLTS Job Openings (JTSJOL): Monthly labor market data - Average Hourly Earnings (AHETPI): Monthly wage data - M2 Money Supply (M2SL): Monthly monetary aggregates - Commercial Loans (BUSLOANS): Weekly credit data ### 4.3 Market-Based Indicators - VIX Index: Real-time volatility measure - S&P; 500: Market sentiment proxy - DXY Index: Dollar strength indicator ## 5. Model Validation and Performance ### 5.1 Historical Backtesting (2017-2024) Comprehensive backtesting across multiple Fed policy cycles demonstrates: - Signal Accuracy: 78% correct directional predictions - Timing Precision: 2.3 meetings average lead time - Crisis Detection: 100% accuracy in identifying emergency measures - False Signal Rate: 12% (within acceptable research parameters) ### 5.2 Regime-Specific Performance Tightening Cycles (2017-2018, 2022-2023): - Hawkish signal accuracy: 82% - Average prediction lead: 1.8 meetings - False positive rate: 8% Easing Cycles (2019, 2020, 2024): - Dovish signal accuracy: 85% - Average prediction lead: 2.1 meetings - Crisis mode detection: 100% Neutral Periods: - Hold prediction accuracy: 73% - Regime stability detection: 89% ### 5.3 Comparative Analysis AFDFM performance compared to alternative methods: - Fed Funds Futures: Similar accuracy, lower lead time - Economic Surveys: Higher accuracy, comparable timing - Simple Taylor Rule: Lower accuracy, insufficient complexity - Market-Based Models: Similar performance, higher volatility ## 6. Practical Applications and Use Cases ### 6.1 Institutional Investment Management - Fixed Income Portfolio Positioning: Duration and curve strategies - Currency Trading: Dollar-based carry trade optimization - Risk Management: Interest rate exposure hedging - Asset Allocation: Regime-based tactical allocation ### 6.2 Corporate Treasury Management - Debt Issuance Timing: Optimal financing windows - Interest Rate Hedging: Derivative strategy implementation - Cash Management: Short-term investment decisions - Capital Structure Planning: Long-term financing optimization ### 6.3 Academic Research Applications - Monetary Policy Analysis: Fed behavior studies - Market Efficiency Research: Information incorporation speed - Economic Forecasting: Multi-factor model validation - Policy Impact Assessment: Transmission mechanism analysis ## 7. Model Limitations and Risk Factors ### 7.1 Data Dependency - Revision Risk: Economic data subject to subsequent revisions - Availability Lag: Some indicators released with delays - Quality Variations: Market disruptions affect data reliability - Structural Breaks: Economic relationship changes over time ### 7.2 Model Assumptions - Linear Relationships: Complex non-linear dynamics simplified - Parameter Stability: Component weights may require recalibration - Regime Classification: Subjective threshold determinations - Market Efficiency: Assumes rational information processing ### 7.3 Implementation Risks - Technology Dependence: Real-time data feed requirements - Complexity Management: Multi-component coordination challenges - User Interpretation: Requires sophisticated economic understanding - Regulatory Changes: Fed framework evolution may require updates ## 8. Future Research Directions ### 8.1 Machine Learning Integration - Neural Network Enhancement: Deep learning pattern recognition - Natural Language Processing: Fed communication sentiment analysis - Ensemble Methods: Multiple model combination strategies - Adaptive Learning: Dynamic parameter optimization ### 8.2 International Expansion - Multi-Central Bank Models: ECB, BOJ, BOE integration - Cross-Border Spillovers: International policy coordination - Currency Impact Analysis: Global monetary policy effects - Emerging Market Extensions: Developing economy applications ### 8.3 Alternative Data Sources - Satellite Economic Data: Real-time activity measurement - Social Media Sentiment: Public opinion incorporation - Corporate Earnings Calls: Forward-looking indicator extraction - High-Frequency Transaction Data: Market microstructure analysis ## References Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404. Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945. Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43. Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707. Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52. Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478. Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26. Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System. Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150. Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022. Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City. Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214. Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley. --- Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model. Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
Pine Script® indicator
by EdgeTools
Vortex Pivot Strategy (VPS)Strategy Overview: This custom indicator is designed around a powerful contrarian trading philosophy: capitalize on market-wide pessimism among both short-term and mid-term traders, and enter positions at historically high-probability bounce zones using pivot levels. The setup combines three core ideas: A clear downtrend structure, where short- and mid-term participants are in loss. Entry at S3 pivot support, which statistically represents extreme oversold zones. A quick, rational exit at the central pivot level, minimizing holding time and maximizing reward-to-risk efficiency. 📈 Conditions for Entry (Buy Setup): 50-day SMA above 20-day SMA, which is above the current price. This sequence implies that mid-term traders (50-day SMA) are in loss, short-term traders (20-day SMA) are in loss, and price has dropped below both — indicating peak pessimism and fear. Price must touch or dip below the S3 pivot level (from the Pivot Points Standard - Weekly). S3 is considered an extreme support zone. When price touches it while the SMA structure confirms maximum bearish sentiment, it sets up a high-probability bounce scenario. 🎯 Exit Strategy (Target): The central Pivot Point (P) becomes your exit level. Since the price is entering from a deeply oversold region, a reversion to the weekly pivot is statistically probable. This ensures the trade remains quick, directional, and avoids greed-based exits. 💡 Why This Works (Psychology & Edge): This is a player-versus-player game. When you buy during a setup like this, you're essentially buying when the majority of active traders are in pain: Mid-term traders (50 SMA) are holding positions at higher levels — they’re sitting in drawdown. Short-term traders (20 SMA) are also underwater. Panic is widespread. Volume dries up. Selling is largely exhausted. Meanwhile, you're entering a fundamentally strong stock at a deeply discounted price, and aiming for a modest reversion — not an unrealistic uptrend continuation. That gives you both psychological and statistical edge. You're not trying to predict a reversal — you're positioning against fear and riding the natural bounce that follows. 🔧 How to Use This Indicator: Add this indicator to a Daily timeframe chart of fundamentally strong stocks (you should do your own fundamental screening). Wait for the condition: SMA stack = 50 > 20 > Price AND price touches S3. The script will automatically draw a horizontal line at the entry (S3) and the target (Pivot). Once triggered, take the trade and exit around the Pivot level. Optional: you can use manual averaging or position sizing based on your risk strategy since fundamentally strong stocks typically revert over time.
Pine Script® indicator
by EmonMalik
1
1x RVOL Bull/Bear Painter REVERSAL CATCHThis powerful Indicator Paints the candle if there is a Relative Volume of 1.5 or higher. Notice that if you mark the high of the last Bullish RVOL candle (blue candle) a power reversal begins at that same price. This does the same thing for the Bearish RVOL Candle. If you mark the low of that candle (purple), a reversal begins at that price. This can be used on any time frame, but using it on higher time frames catches you HUGE SWINGS
Pine Script® indicator
by TheTradingGoddess
1
Multi-Timeframe Entry OKThis indicator performs a multi-timeframe trend-and-momentum check across three timeframes—Daily (“D”), 4-hour (“240”), and 1-hour (“60”). For each timeframe it verifies: • EMA(9) > EMA(20) • MACD histogram > 0 (MACD parameters 12,26,9) If all three timeframe conditions are true, it returns 1; otherwise it returns 0. Use case: 1. Save and publish this script with “Add to Screener” enabled. 2. In Crypto Screener, go to Filters → Custom and add “Multi-Timeframe Entry OK”. 3. Filter for “Multi-Timeframe Entry OK == 1” to automatically extract symbols that satisfy EMA and MACD trend alignment on Daily, 4H, and 1H. Parameters: – EMA periods fixed at 9 and 20 – MACD fixed at 12,26,9 – No external inputs required This automates multi-timeframe AND logic, ensuring only symbols with aligned trend and momentum on longer and shorter timeframes appear in your screener.
Pine Script® indicator
by shinya_morimoto79
💣 Rounded Top Short Signal💣R3KT is your scalp short assassin — locking on to rounded tops and detonating precision sell signals with zero lag. Built to expose weak highs before they collapse, it combines 3-bar swing top detection with a bearish momentum cross, then marks the kill zone with a clean 💣 emoji. No clutter. No lag. Just surgical entries where the bulls die slow. 🔍 Signal Criteria: 3-bar rounded top structure Bearish WaveTrend crossunder Bomb emoji plotted — no background, sniper-ready 🧠 Optimized for Heikin Ashi: 💣R3KT performs with maximum accuracy on Heikin Ashi candles, where smoothed price action enhances signal clarity, trend momentum, and rounded top formation. Use HA for signal detection, and standard candles for entry and execution. Scalp sharp. Hit fast. 💣R3KT doesn’t warn — it executes.
Pine Script® indicator
by deathburka