#3889 Stock Indicator Look-Ahead Gain/Loss Documenter

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#3889 Stock Indicator Look-Ahead Gain/Loss Documenter

#3889 Help me find a technical analysis pattern documenter for stock price historical data. For each data point, calculate the values for a user-specified list of indicators, then look ahead 1, 2, 3, .. n periods and log the price change for each indicator['value']. The end result should be a way to list the indicator['value']s that produce the best gains or losses for each look-ahead period.

This is a sophisticated request. What you are describing is technically known as quantitative factor analysis or signal decay analysis.

There is no single off-the-shelf software named "Pattern Documenter" that does exactly this custom logic out of the box. However, you can build this tool yourself using Python. This is the standard approach used by "quants" (quantitative analysts) to mathematically prove which indicators actually predict future price movement.

Below is a complete, custom Python solution that functions as your Technical Analysis Pattern Documenter.

The Solution: "The Pattern Documenter" Script

This script uses:

  • yfinance: To get free historical stock data.

  • pandas-ta: To calculate the indicators (RSI, MACD, SMA, etc.).

  • pandas: To perform the "look-ahead" vector logic (much faster than iterating row-by-row).

Prerequisites

You will need to install these libraries:

pip install pandas yfinance pandas_ta

Python Code

Copy this code into a file named pattern_documenter.py.

import yfinance as yf
import pandas as pd
import pandas_ta as ta
import numpy as np

def fetch_data(ticker, period="5y", interval="1d"):
    """Fetches historical data from Yahoo Finance."""
    print(f"Fetching data for {ticker}...")
    df = yf.Ticker(ticker).history(period=period, interval=interval)
    return df

def add_indicators(df):
    """
    USER CONFIGURATION: Add your list of indicators here.
    Using pandas-ta for easy calculation.
    """
    # Example 1: RSI (14 periods)
    df.ta.rsi(length=14, append=True)
    
    # Example 2: SMA (50 periods) - We will normalize this as % distance from price
    # Raw SMA is useless for grouping; we need "Price vs SMA" relation
    df['SMA_50'] = ta.sma(df['Close'], length=50)
    df['Dist_SMA_50'] = (df['Close'] - df['SMA_50']) / df['SMA_50'] * 100

    # Example 3: Bollinger Band Width (volatility)
    df.ta.bbands(length=20, append=True)
    df['BB_Width'] = (df['BBU_20_2.0'] - df['BBL_20_2.0']) / df['BBM_20_2.0']

    return df

def perform_lookahead_analysis(df, lookahead_periods=[1, 2, 3, 5, 10]):
    """
    Calculates future price changes for each specified look-ahead period.
    """
    # Create "Future Return" columns
    for n in lookahead_periods:
        # Shift data backwards to align future price with current row
        # (Current Row) -> (Price in N days)
        future_close = df['Close'].shift(-n)
        
        # Calculate percent change
        col_name = f'Return_{n}d'
        df[col_name] = ((future_close - df['Close']) / df['Close']) * 100
    
    return df

def document_patterns(df, indicator_col, bins=10):
    """
    Groups the data by the indicator value and calculates the average
    gain/loss for the look-ahead periods.
    """
    clean_df = df.dropna(subset=[indicator_col])
    
    # Create "bins" (ranges) for the indicator values
    # e.g., RSI 0-10, 10-20, 20-30...
    try:
        clean_df['Range'] = pd.cut(clean_df[indicator_col], bins=bins)
    except ValueError:
        # Fallback for indicators with low variance or discrete values
        clean_df['Range'] = pd.qcut(clean_df[indicator_col], q=bins, duplicates='drop')

    # Calculate average return for each bin across all look-ahead columns
    return_cols = [c for c in df.columns if 'Return_' in c]
    
    # Group by the range and take the mean of future returns
    stats = clean_df.groupby('Range', observed=False)[return_cols].mean()
    
    # Add a count to see how often this pattern occurs
    stats['Count'] = clean_df.groupby('Range', observed=False)[indicator_col].count()
    
    return stats

# --- MAIN EXECUTION ---
if __name__ == "__main__":
    # 1. Settings
    TICKER = "SPY"
    INDICATOR_TO_ANALYZE = "RSI_14"  # Change this to 'Dist_SMA_50' or others defined above
    
    # 2. Process
    data = fetch_data(TICKER)
    data = add_indicators(data)
    data = perform_lookahead_analysis(data, lookahead_periods=[1, 3, 5, 10])
    
    # 3. Analyze
    print(f"\n--- Pattern Analysis for {TICKER}: {INDICATOR_TO_ANALYZE} ---")
    results = document_patterns(data, INDICATOR_TO_ANALYZE, bins=10)
    
    # Formatting for readability
    pd.set_option('display.max_columns', None)
    pd.set_option('display.width', 1000)
    print(results.sort_index(ascending=False)) # Show highest indicator values first
    
    print("\nInterpreting the Output:")
    print("If 'Return_5d' is 2.5% for the RSI bin (20, 30], it means:")
    print("Historically, when RSI was between 20 and 30, the price was 2.5% higher 5 days later on average.")

How to Use This Tool

  1. Define Your Indicators: Edit the add_indicators function. You can use any indicator supported by pandas-ta (MACD, ADX, ATR, etc.).

  2. Choose Your Target: Change INDICATOR_TO_ANALYZE in the Main Execution section to matches the column name of the indicator you want to test (e.g., 'RSI_14').

  3. Run: Execute the script. It will output a table that groups your indicator into ranges (e.g., RSI 20-30, 30-40) and shows the average future return for 1, 3, 5, and 10 days later.

Interpreting the Results

The script will output a table that looks like this:

RangeReturn_1dReturn_3dReturn_5dCount
(20.0, 30.0]0.45%1.20%2.10%54
(30.0, 40.0]0.10%0.30%0.50%120
(70.0, 80.0]-0.20%-0.60%-1.10%60

The Strategy: Look for "skewed" rows.

  • Green Flag: If the (20.0, 30.0] row shows consistently high positive returns across 1d, 3d, and 5d, you have mathematically documented that "When RSI is between 20 and 30, this asset tends to rise."

  • Red Flag: If high RSI values (70+) show negative returns in the look-ahead columns, you have confirmed a mean-reversion pattern (price drops after being overbought).

Going Pro: Advanced Alternatives

If you want to take this beyond a script and use professional-grade libraries designed specifically for this "Factor Analysis," look into:

  1. Alphalens-reloaded: This is the industry-standard Python library (formerly by Quantopian) specifically for analyzing how well an "alpha factor" (your indicator) predicts future returns. It produces "tear sheets" with beautiful charts of exactly what you asked for.

  2. VectorBT: A high-performance library that allows you to run this kind of analysis on millions of data points in seconds using vectorized operations.

Relevant Video: Quantstats Python Library

This video demonstrates using Python libraries to analyze strategy performance metrics, which is the next logical step after documenting your patterns.


From <https://gemini.google.com/app/> Google Gemini 3.0 (Thinking)

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