Projects

Pattern-Driven BTC Futures Trading Bot

Algorithmic trading system achieving profitability through systematic pattern analysis

POC
+0.55%
ROI
22.4%
Win Rate
+$0.68
Expected Value
Finance & Trading
Python, APIs, Quantitative
Proof of Concept
CHALLENGE

Pattern Recognition Complexity

Cryptocurrency futures markets exhibit complex patterns that require systematic analysis. Manual pattern identification is inconsistent, and traditional strategies often fail to adapt to changing market conditions.

Manual pattern analysis is inconsistent
Markets shift between multiple regimes
Need for systematic risk management
SOLUTION

Multi-Regime Pattern System

Systematic trading system analyzing 395 trades to discover 6 critical optimizations. Achieved profitability with 22.4% win rate and 4.75:1 reward-to-risk ratio through multi-regime adaptation.

Pattern-driven analysis across 395 trades
Multi-regime adaptation system
10x faster iteration with AI assistance

Business Impact

+0%
ROI
Positive return on investment
0.4%
Win Rate
With 4.75:1 reward-to-risk ratio
+$0
Expected Value
Per trade across 395 trades

Technical Architecture

Trading
Python
Deribit API
Binance API
Analysis
Quantitative Finance
pandas

Framework & Approach

Pattern-driven optimization analyzing 395 trades across 10 dimensions. Discovered counterintuitive insight: 22% WR with 4.75:1 R:R beats 30% WR with 1.5:1 R:R. Multi-timeframe validation revealed scope limitations.

1

Phase 1A: Data Infrastructure - Deribit/Binance API integration, data normalization, historical data caching system

2

Phase 1B: Strategy Framework - Momentum/mean reversion strategies with volatility regime detection

3

Phase 1C: Optimization - Pattern analysis across 395 trades discovering 6 critical optimizations

4

Phase 1D: Validation - Multi-timeframe testing (5-minute and hourly data across 5 years) to find limitations

5

Phase 2: Paper Trading - IBKR testnet validation (planned)

6

Phase 3: Live Trading - Production deployment with risk management (future if validation successful)

What This Project Demonstrates

Transferable skills and capabilities beyond the technical implementation

Pattern Analysis Over Parameter Optimization

Initial strategy losing (-2.45% ROI). Analyzed 395 historical trades for patterns instead of blind parameter tuning. Discovered 6 critical patterns, achieved +0.55% ROI through targeted optimization.

Data AnalysisPattern RecognitionRoot Cause Analysis

Counterintuitive Insights Require Domain Knowledge

Lower win rate (22%) with higher R:R (4.75:1) is MORE profitable than higher win rate (30%) with poor R:R (1.5:1). Domain expertise: knew to focus on expected value, not win rate.

Domain ExpertiseQuantitative AnalysisCounterintuitive Thinking

Multi-Timeframe Validation

Short-term validation showed profitability. Tested on 5-minute AND hourly data across 5 years. Discovered timeframe-specific behavior - works for scalping, not swing trading.

Validation RigorLimitation DiscoveryHonest Reporting

AI Acceleration with Human Judgment

AI tested 631 trades in hours (would take weeks manually). Human decided stop loss widening despite lower win rate. Result: R:R improved 3x, strategy became profitable.

AI LeverageHuman JudgmentDecision Making