Ichimoku Trading Series: Part 1 of 10 | View Full Series
What You Will Build
In this 10-part series, you will learn to automate a trading strategy that combines:
- Ichimoku Cloud (Ichimoku Kinko Hyo) for entry signals
- EMA 100 as a trend filter
- ATR-based stop-loss and take-profit management
The strategy achieved ~40% yearly returns in backtests across multiple FX pairs on the 4-hour timeframe, with a 53-69% win rate and controlled drawdowns.
Why This Strategy Works
The core principle is simple but powerful:
- Trade WITH the trend — The EMA filter ensures we only take trades in the prevailing market direction
- Wait for retracements — The Ichimoku Cloud identifies perfect pullback entries
- Enter at the bounce — When price dips INTO the cloud and closes OUTSIDE, the retracement is likely over
Visual Signal Example
Green triangles = Long signals (buy)
Red triangles = Short signals (sell)The strategy only takes trades that align with both the EMA trend AND the Ichimoku setup.
What Makes This Approach Unique
Problem with Standard Ichimoku
Most Ichimoku implementations suffer from look-ahead bias — they shift the cloud forward in time, which gives misleadingly good backtest results.
Our Solution
We compute the Ichimoku components without forward-shifting the spans, ensuring honest backtesting results that translate to real trading.
Key Backtest Results
| Metric | Value |
|---|---|
| Annual Return | 28-43% |
| Win Rate | 53-69% |
| Max Drawdown | -6% to -21% |
| Sharpe Ratio | 1.0-1.38 |
| Trades per Year | ~13 (selective) |
Important Note: The strategy is SELECTIVE — only ~13 trades per year. This is a feature, not a bug. Quality over quantity.
Course Overview
Over the next 10 parts, you will learn:
- Part 2: The Five Ichimoku Components
- Part 3: Understanding the Kumo Cloud
- Part 4: EMA Trend Filter
- Part 5: Entry Signal Conditions
- Part 6: Trade Management with ATR
- Part 7: Python Backtesting Setup
- Part 8: Building the Strategy Class
- Part 9: Parameter Optimization
- Part 10: Results Analysis & Next Steps
Prerequisites
- Basic Python knowledge
- Understanding of candlestick charts
- Familiarity with trading concepts (SL, TP, R:R)