stock-bot/docs/focus.md

Based on my experience and research, here's a comprehensive breakdown of strategy development components and where you should focus:

The Complete Strategy Development Pipeline

1. Idea Generation & Hypothesis Formation (15% of effort)

What it involves:

• Market microstructure understanding
• Economic rationale for the edge
• Literature review and academic research
• Observing market inefficiencies

Focus Level: MEDIUM

• Don't over-research; many good ideas are simple
• Ensure there's a logical reason WHY the strategy should work
• Avoid pure data mining without economic rationale

2. Data Infrastructure (25% of effort)

Critical Components:

• Data quality and cleaning
• Survivorship bias handling
• Corporate actions adjustment
• Proper point-in-time data

Focus Level: VERY HIGH ⭐

• This is where most strategies fail in production
• Bad data = invalid backtests = losing money
• Look-ahead bias is the silent killer
• The time-series nature of financial datasets limits the effective amount of data available to train, validate and retrain models since special care must be taken not to include future data in any way

3. Feature Engineering (20% of effort)

Key Areas:

• Market microstructure features (order flow, volume profiles)
• Cross-sectional features (relative value metrics)
• Alternative data integration
• Regime indicators

Focus Level: HIGH

• Features matter more than models
• Domain expertise pays off here
• Keep features interpretable when possible

4. Strategy Logic & Signal Generation (15% of effort)

Components:

• Entry/exit rules
• Position sizing algorithms
• Risk limits and constraints
• Portfolio construction

Focus Level: MEDIUM

• Simpler is often better
• Complexity should come from combining simple, robust signals
• Avoid overfitting with too many rules

5. Backtesting Framework (10% of effort)

Essential Elements:

• Transaction cost modeling
• Market impact estimation
• Proper execution assumptions
• Realistic capacity constraints

Focus Level: HIGH ⭐

• Most backtests are too optimistic
• Walk forward analysis only tests a single price path whereas other tests such as Noise testing, Vs Shifted, Variance testing or Monte Carlo Permutation test multiple price paths
• Focus on realistic execution assumptions

6. Statistical Validation (10% of effort)

Including:

• Permutation tests (as discussed)
• Out-of-sample testing
• Statistical significance tests
• Robustness checks

Focus Level: MEDIUM-HIGH

• Important but don't over-optimize
• Testing for overfitting at the earliest possible stage

7. Risk Management (5% of effort but 90% of survival)

Critical Aspects:

• Drawdown controls
• Correlation management
• Tail risk hedging
• Position limits

Focus Level: VERY HIGH ⭐

• This determines survival
• Good risk management saves bad strategies
• Bad risk management kills good strategies

Where You Should REALLY Focus

🎯 Priority 1: Data Quality & Infrastructure

Why:

• 80% of production failures come from data issues
• It's unsexy but absolutely critical
• Garbage in = garbage out

Specific Actions:

• Build robust data pipelines
• Implement comprehensive data quality checks
• Create point-in-time data snapshots
• Test for survivorship bias

🎯 Priority 2: Transaction Costs & Market Impact

Why:

• The difference between paper and real trading
• Can turn profitable strategies unprofitable
• Often underestimated in backtests

Key Considerations:

• Bid-ask spreads during your trading times
• Market impact models for your size
• Slippage estimates based on real execution data
• Hidden costs (borrow costs for shorts, etc.)

🎯 Priority 3: Regime Awareness

Why:

• Markets change; strategies that don't adapt die
• Market dynamics can change, but there is no solution to this risk

Implementation:

• Build regime detection systems
• Adjust position sizing by regime
• Have strategy on/off switches
• Monitor strategy degradation metrics

Common Traps to Avoid

1. Over-Optimization

• Too many parameters = overfitting
• It is highly recommended for your strategy to have as little configurable parameters (degrees of freedom) as possible

2. Selection Bias

• Testing 1000 strategies and picking the best
• Not accounting for multiple testing
• One of the permutation tests created by the author detected a hidden selection bias problem in a trading system

3. Ignoring Capacity

• Strategy works with $100k but not $10M
• Market impact kills returns at scale
• Liquidity constraints binding

4. Complexity Bias

• Complex != better
• Simple strategies are more robust
• Many strategies that look profitable actually perform just as well on completely random data

Modern Best Practices (2025)

1. Machine Learning Integration

• Use ML for feature selection, not just prediction
• Ensemble methods for robustness
• Deep generative models to produce synthetic time-series data, enhancing the amount of data available for training

2. Real-Time Monitoring

• Live performance tracking vs. backtest
• Automatic strategy shutdown triggers
• A/B testing framework for improvements

3. Alternative Data

• Sentiment analysis
• Satellite data
• Web scraping (where legal)
• But validate the alpha decay

4. Execution Alpha

• Smart order routing
• Optimal execution algorithms
• Dark pool access
• This is often easier edge than signal alpha

Recommended Development Process

Phase 1: Research (2-4 weeks)

1. Hypothesis formation with economic rationale
2. Initial data exploration
3. Simple prototype testing
4. Go/No-go decision

Phase 2: Development (4-8 weeks)

1. Full data pipeline build
2. Feature engineering
3. Strategy implementation
4. Initial backtesting

Phase 3: Validation (2-4 weeks)

1. Permutation tests
2. Out-of-sample testing
3. Sensitivity analysis
4. Go/No-go decision

Phase 4: Production Prep (2-4 weeks)

1. Execution infrastructure
2. Risk management systems
3. Monitoring and alerting
4. Paper trading

Phase 5: Go-Live (Ongoing)

1. Gradual position scaling
2. Live performance monitoring
3. Continuous improvement
4. Regular strategy review

The Reality Check

What Actually Matters Most:

1. Data quality (can't emphasize enough)
2. Transaction costs (the silent killer)
3. Risk management (determines survival)
4. Execution quality (often overlooked)
5. Regime adaptability (markets change)

What's Often Overemphasized:

1. Complex models (simple often better)
2. Perfect optimization (robustness > perfection)
3. High Sharpe ratios in backtest (usually unrealistic)
4. Academic purity (markets are messy)

Final Advice

Focus on building robust, simple strategies with:

• Clean data pipelines
• Realistic execution assumptions
• Strong risk management
• Adaptability to changing markets

Remember: Over 90% of strategies that look amazing fail in production. The difference between success and failure is usually in the unglamorous details of data quality, execution, and risk management, not in having the most sophisticated model.

Start simple, test thoroughly, and scale gradually. The market will teach you humility quickly enough.