Ichimoku Cloud + EMA Trading Strategy
A comprehensive 10-part course teaching you to build, backtest, and optimize an algorithmic trading strategy combining Ichimoku Cloud with EMA trend filtering.
Course Highlights
- Timeframe: 4-Hour Charts
- Expected Returns: 28-43% annually (backtested)
- Win Rate: 53-69%
- Language: Python
Course Contents
| Part | Title | Topics |
|---|---|---|
| 1 | Introduction | Strategy overview, expected results, why it works |
| 2 | The Five Components | Tenkan, Kijun, Senkou Spans, Chikou explained |
| 3 | The Kumo Cloud | Bullish/bearish clouds, avoiding look-ahead bias |
| 4 | EMA Trend Filter | 100-period filter rules, code implementation |
| 5 | Entry Signals | Cloud pierce conditions, combining filters |
| 6 | Trade Management | ATR-based SL/TP, risk-reward optimization |
| 7 | Backtesting Setup | Python environment, data fetching |
| 8 | Strategy Class | backtesting.py implementation |
| 9 | Optimization | Grid search, heat maps, multi-asset testing |
| 10 | Results & Next Steps | Final analysis, deployment options |
Prerequisites
- Basic Python knowledge
- Understanding of candlestick charts
- Familiarity with trading concepts (SL, TP, R:R)
What You Will Learn
- Build a complete algorithmic trading strategy
- Avoid common backtesting pitfalls (look-ahead bias)
- Optimize parameters systematically
- Deploy strategies as signal generators
Source Materials
- Video Tutorial: Ziad Francis – Algorithmic Trading
- Code Repository: GitHub – Ichimoku_Trend