Strategy Prioritization

Quick Start

When prioritizing strategies:

• Inventory all available strategies across codebase

• Score each strategy on 4 factors (Performance, Risk, Operations, Business)

• Rank strategies by composite score

• Generate deployment recommendations

• Identify gaps blocking promotion

Scoring Framework

Four-Factor Scoring (1-5 scale, equal weights)

Composite Score = (Performance + Risk + Ops + Business) / 4

1. Performance (25%)

• 5: Strong metrics (Sharpe > 2.0, Win Rate > 70%, PF > 2.5)

• 4: Good metrics (Sharpe > 1.5, Win Rate > 65%, PF > 2.0)

• 3: Moderate or limited data

• 2: Weak metrics or no recent backtests

• 1: No performance data

2. Risk Readiness (25%)

• 5: Comprehensive controls (stops, sizing, limits, correlation)

• 4: Good controls (stops, sizing, basic limits)

• 3: Basic controls (stops only)

• 2: Minimal controls

• 1: No risk management

3. Operational Readiness (25%)

• 5: Fully configured, tested, documented, monitored

• 4: Configured and tested, minor doc gaps

• 3: Basic config, needs testing/docs

• 2: Code exists, not configured

• 1: Experimental/incomplete

4. Business Importance (25%)

• 5: Explicitly recommended, high priority, proven

• 4: Important, good business case

• 3: Moderate value

• 2: Low priority/experimental

• 1: Example/research only

Prioritization Process

Step 1: Strategy Discovery

Find all strategies

find openalgo/strategies/scripts -name ".py" -type f find openalgo_backup_/strategies/scripts -name ".py" -type f
find AITRAPP/AITRAPP/packages/core/strategies -name ".py" -type f

Check documentation

grep -r "strategy" *.md | grep -i "priorit|rank|recommend"

Step 2: Data Collection

For each strategy, gather:

Performance Data:

• Backtest results from openalgo/strategies/backtest_results/

• Metrics from ALL_STRATEGIES_COMPARISON.md

• Ranking reports and CSV files

• AITRAPP backtest engine results

Risk Assessment:

Check for risk controls in code

grep -r "stop_loss|max_drawdown|position_size|risk_per_trade" strategy_file.py grep -r "daily_loss_limit|weekly_loss_limit|correlation" strategy_file.py

Operational Check:

• Config files: AITRAPP/AITRAPP/configs/app.yaml

• Deployment scripts: openalgo/strategies/scripts/

• Documentation: Strategy .md files

• Monitoring: Log files, status endpoints

Business Value:

• Check STRATEGY_PRIORITIZATION_REPORT.md

• Review ALL_STRATEGIES_COMPARISON.md recommendations

• Look for explicit deployment recommendations

Step 3: Scoring

def score_strategy(strategy_name, performance_data, risk_data, ops_data, business_data): """Score strategy on 4 factors""" perf_score = score_performance(performance_data) # 1-5 risk_score = score_risk(risk_data) # 1-5 ops_score = score_operations(ops_data) # 1-5 biz_score = score_business(business_data) # 1-5

composite = (perf_score + risk_score + ops_score + biz_score) / 4.0

return {
    'name': strategy_name,
    'performance': perf_score,
    'risk': risk_score,
    'operations': ops_score,
    'business': biz_score,
    'composite': composite,
    'gaps': identify_gaps(perf_score, risk_score, ops_score, biz_score)
}

Step 4: Ranking and Categorization

def categorize_strategy(composite_score): """Categorize by action needed""" if composite_score >= 4.0: return "Deploy", "Ready for live trading" elif composite_score >= 3.0: return "Paper Trade", "Needs validation" elif composite_score >= 2.5: return "Optimize", "Needs improvements" else: return "Hold", "Experimental or incomplete"

Step 5: Generate Report

Create prioritization report with:

• Ranked table (sorted by composite score)

• Detailed analysis per strategy

• Gap identification

• Deployment roadmap

• Action items

Key Metrics Reference

Performance Metrics

Sharpe Ratio:

• Excellent: > 2.0

• Good: 1.5 - 2.0

• Acceptable: 1.0 - 1.5

• Poor: < 1.0

Win Rate:

• Excellent: > 70%

• Good: 60-70%

• Acceptable: 50-60%

• Poor: < 50%

Profit Factor:

• Excellent: > 2.5

• Good: 2.0 - 2.5

• Acceptable: 1.5 - 2.0

• Poor: < 1.5

Max Drawdown:

• Excellent: < 10%

• Good: 10-15%

• Acceptable: 15-20%

• Poor: > 20%

Risk Controls Checklist

• Stop loss implemented

• Position sizing based on risk

• Daily loss limit

• Weekly loss limit

• Max drawdown protection

• Correlation management

• Max positions limit

• Volatility-based sizing

Operational Checklist

• Configuration file exists

• Parameters documented

• Deployment script available

• Logging implemented

• Monitoring integrated

• Error handling robust

• Documentation complete

• Tested in sandbox

Integration Points

With Backtesting

• Use backtest results to score performance

• Reference backtesting-analysis skill for metrics

• Check openalgo/strategies/backtest_results/ for data

With Strategy Management

• Coordinate deployment with strategy-manager subagent

• Check current running strategies before prioritizing

• Verify strategy status via web UI

With Risk Management

• Align with risk-management skill requirements

• Verify risk controls meet standards

• Check portfolio-level constraints

Common Patterns

High-Priority Strategies

Look for:

• Documented backtests with strong metrics

• Comprehensive risk controls

• Fully configured and tested

• Explicitly recommended in docs

Strategies Needing Work

Identify:

• Missing backtest data → Run backtests

• Weak risk controls → Add risk management

• Configuration gaps → Create configs

• Documentation gaps → Write docs

Archived Strategies

• Check openalgo_backup_*/strategies/ for high-performing archived strategies

• Consider porting to current location if score is high

• Verify code compatibility before promotion

Report Template

Strategy Prioritization Plan - [Date]

Executive Summary

• Total strategies: X
• Top 3: [List]
• Ready to deploy: X
• Need work: X

Ranked Strategies

Detailed Analysis

Strategy A

Performance (5/5): [Details] Risk (5/5): [Details] Operations (4/5): [Details] Business (5/5): [Details] Gaps: None Next Steps: Deploy to live trading

Gaps Blocking Promotion

• Strategy X: Missing backtest data
• Strategy Y: No risk controls

Deployment Roadmap

1. Week 1: Deploy top 3 strategies
2. Week 2: Paper trade next tier
3. Month 1: Optimize remaining strategies

Best Practices

• Be Conservative: When data is missing, score low and mark as gap

• Prioritize Data: Strategies with documented performance rank higher

• Actionable Output: Provide specific next steps, not just scores

• Regular Updates: Re-prioritize as strategies are tested/deployed

• Document Gaps: Clearly identify blockers to enable promotion

• Consider Context: Market conditions and instrument types matter

Troubleshooting

Missing Performance Data

• Run backtests using backtesting-analysis skill

• Check archived backtest results

• Look for comparison reports

Incomplete Risk Controls

• Reference risk-management skill for requirements

• Add missing controls before promotion

• Test risk limits in sandbox

Configuration Issues

• Check existing configs in AITRAPP/AITRAPP/configs/

• Create config files following patterns

• Verify parameters are documented

Related Resources

• Subagent: strategy-prioritization-planner for detailed planning

• Skill: backtesting-analysis for performance metrics

• Skill: risk-management for risk control standards

• Skill: trading-strategy-development for strategy structure

• Reports: STRATEGY_PRIORITIZATION_REPORT.md , ALL_STRATEGIES_COMPARISON.md