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SamoraDCSamoraDC
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trying to fix the backtesting that are identifying 0 alphas
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‎analysis/quick_rsi_analysis.sh‎

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#!/bin/bash
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# Quick RSI Analysis using Python with system packages
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cd /mnt/c/Users/DaviCastroSamora/Documents/SamoraDC/RustAlgorithmTrading
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# Create venv if not exists
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if [ ! -d "venv" ]; then
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echo "Creating virtual environment..."
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python3 -m venv venv
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fi
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# Activate and install dependencies
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source venv/bin/activate
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pip install --quiet pandas pyarrow numpy 2>&1 | grep -v "Requirement already"
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echo "==================== RSI SIGNAL ANALYSIS ===================="
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echo ""
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# Run quick analysis
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python3 << 'EOF'
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import pandas as pd
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import numpy as np
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import json
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from pathlib import Path
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symbols = ['AAPL', 'MSFT', 'GOOGL']
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results = {}
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print("LOADING & ANALYZING DATA...\n")
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for symbol in symbols:
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# Load parquet
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df = pd.read_parquet(f'data/historical/{symbol}.parquet')
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# Calculate RSI(14)
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delta = df['close'].diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
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rs = gain / loss
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rsi = 100 - (100 / (1 + rs))
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rsi_valid = rsi.dropna()
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# Statistics
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stats = {
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'min': float(rsi_valid.min()),
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'max': float(rsi_valid.max()),
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'mean': float(rsi_valid.mean()),
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'median': float(rsi_valid.median()),
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'std': float(rsi_valid.std()),
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'q25': float(rsi_valid.quantile(0.25)),
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'q75': float(rsi_valid.quantile(0.75)),
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}
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# Threshold analysis
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oversold_35 = (rsi_valid <= 35).sum()
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overbought_65 = (rsi_valid >= 65).sum()
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oversold_30 = (rsi_valid <= 30).sum()
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overbought_70 = (rsi_valid >= 70).sum()
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# Crossings
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cross_35_down = ((rsi_valid.shift(1) > 35) & (rsi_valid <= 35)).sum()
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cross_65_up = ((rsi_valid.shift(1) < 65) & (rsi_valid >= 65)).sum()
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cross_30_down = ((rsi_valid.shift(1) > 30) & (rsi_valid <= 30)).sum()
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cross_70_up = ((rsi_valid.shift(1) < 70) & (rsi_valid >= 70)).sum()
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results[symbol] = {
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'bars': len(rsi_valid),
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'rsi_stats': stats,
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'current_params_35_65': {
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'oversold_bars': int(oversold_35),
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'overbought_bars': int(overbought_65),
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'oversold_crossings': int(cross_35_down),
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'overbought_crossings': int(cross_65_up),
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'total_signals': int(cross_35_down + cross_65_up)
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},
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'industry_params_30_70': {
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'oversold_bars': int(oversold_30),
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'overbought_bars': int(overbought_70),
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'oversold_crossings': int(cross_30_down),
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'overbought_crossings': int(cross_70_up),
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'total_signals': int(cross_30_down + cross_70_up)
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}
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}
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print(f"{'='*60}")
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print(f"{symbol} - RSI(14) Analysis ({len(rsi_valid)} bars)")
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print(f"{'='*60}")
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print(f"\nRSI Statistics:")
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print(f" Min: {stats['min']:6.2f}")
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print(f" 25%: {stats['q25']:6.2f}")
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print(f" Mean: {stats['mean']:6.2f}")
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print(f" Median: {stats['median']:6.2f}")
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print(f" 75%: {stats['q75']:6.2f}")
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print(f" Max: {stats['max']:6.2f}")
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print(f" StdDev: {stats['std']:6.2f}")
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print(f"\nCurrent Parameters (35/65):")
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print(f" Bars RSI ≤ 35: {oversold_35:3d} ({oversold_35/len(rsi_valid)*100:5.1f}%)")
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print(f" Bars RSI ≥ 65: {overbought_65:3d} ({overbought_65/len(rsi_valid)*100:5.1f}%)")
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print(f" Oversold crossings: {cross_35_down:3d}")
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print(f" Overbought crossings: {cross_65_up:3d}")
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print(f" TOTAL SIGNALS: {cross_35_down + cross_65_up:3d}")
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print(f"\nIndustry Standard (30/70):")
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print(f" Bars RSI ≤ 30: {oversold_30:3d} ({oversold_30/len(rsi_valid)*100:5.1f}%)")
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print(f" Bars RSI ≥ 70: {overbought_70:3d} ({overbought_70/len(rsi_valid)*100:5.1f}%)")
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print(f" Oversold crossings: {cross_30_down:3d}")
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print(f" Overbought crossings: {cross_70_up:3d}")
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print(f" TOTAL SIGNALS: {cross_30_down + cross_70_up:3d}")
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print()
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# Save results
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with open('analysis/rsi_quick_analysis.json', 'w') as f:
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json.dump(results, f, indent=2)
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print(f"\n{'='*60}")
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print("DIAGNOSIS & RECOMMENDATIONS")
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print(f"{'='*60}\n")
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total_signals_35_65 = sum(r['current_params_35_65']['total_signals'] for r in results.values())
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total_signals_30_70 = sum(r['industry_params_30_70']['total_signals'] for r in results.values())
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avg_rsi = np.mean([r['rsi_stats']['mean'] for r in results.values()])
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print(f"Root Cause Analysis:")
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print(f" • Total signals with current params (35/65): {total_signals_35_65}")
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print(f" • Total signals with industry params (30/70): {total_signals_30_70}")
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print(f" • Average RSI across all symbols: {avg_rsi:.2f}")
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print()
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if total_signals_35_65 == 0:
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print("❌ CRITICAL: Current parameters generate ZERO signals!")
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print(" RSI thresholds (35/65) are too tight for this market data.")
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print()
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if avg_rsi > 55:
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print("📈 Market Condition: Bullish bias (elevated RSI)")
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print(" RSI staying above 50 indicates strong uptrend")
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print()
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print("Recommendations:")
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print(" 1. Switch to industry standard thresholds: 30/70")
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print(f" This would generate ~{total_signals_30_70} signals across all symbols")
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print()
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print(" 2. Consider strategy appropriateness:")
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print(" RSI mean-reversion may not suit strong trending markets")
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print()
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print(" 3. Alternative approaches:")
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print(" - Use momentum-following instead of mean-reversion")
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print(" - Add trend filter before RSI signals")
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print(" - Consider asymmetric thresholds (e.g., 25/75)")
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print()
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print(f"Analysis saved to: analysis/rsi_quick_analysis.json")
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print("="*60)
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EOF
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deactivate

‎analysis/rsi_quick_analysis.json‎

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{
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"AAPL": {
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"bars": 237,
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"rsi_stats": {
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"min": 16.74811195024428,
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"max": 96.16295594504986,
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"mean": 54.40716932241217,
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"median": 55.27981767536083,
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"std": 17.885021444132683,
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"q25": 40.52339413164155,
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"q75": 67.55102040816323
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},
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"current_params_35_65": {
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"oversold_bars": 32,
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"overbought_bars": 72,
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"oversold_crossings": 7,
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"overbought_crossings": 14,
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"total_signals": 21
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},
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"industry_params_30_70": {
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"oversold_bars": 21,
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"overbought_bars": 50,
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"oversold_crossings": 3,
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"overbought_crossings": 14,
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"total_signals": 17
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}
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},
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"MSFT": {
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"bars": 237,
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"rsi_stats": {
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"min": 17.542343587970933,
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"max": 96.03529148983694,
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"mean": 55.30377767410985,
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"median": 54.45840813883903,
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"std": 17.834574287814203,
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"q25": 42.70799347471453,
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"q75": 68.53301224162145
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},
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"current_params_35_65": {
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"oversold_bars": 37,
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"overbought_bars": 80,
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"oversold_crossings": 9,
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"overbought_crossings": 9,
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"total_signals": 18
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},
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"industry_params_30_70": {
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"oversold_bars": 23,
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"overbought_bars": 52,
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"oversold_crossings": 13,
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"overbought_crossings": 9,
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"total_signals": 22
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}
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},
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"GOOGL": {
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"bars": 237,
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"rsi_stats": {
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"min": 12.984218077474807,
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"max": 93.25864779874207,
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"mean": 56.46947586005865,
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"median": 56.77535148070595,
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"std": 17.081445801290506,
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"q25": 44.41212121212122,
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"q75": 68.73167871799882
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},
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"current_params_35_65": {
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"oversold_bars": 24,
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"overbought_bars": 74,
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"oversold_crossings": 4,
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"overbought_crossings": 11,
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"total_signals": 15
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},
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"industry_params_30_70": {
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"oversold_bars": 17,
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"overbought_bars": 52,
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"oversold_crossings": 6,
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"overbought_crossings": 11,
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"total_signals": 17
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}
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}
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}

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