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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:

1. Trade WITH the trend — The EMA filter ensures we only take trades in the prevailing market direction
2. Wait for retracements — The Ichimoku Cloud identifies perfect pullback entries
3. 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)

Coming Up Next: In Part 2, we will dive deep into the five Ichimoku components and understand exactly what each line tells us about market structure. Continue to Part 2 →

LLM Architecture Series – Bonus Lesson. In earlier lessons you saw how tokens become vectors. This article goes deeper into what those vectors mean and how simple arithmetic on them can reveal structure in concepts.

Concept vectors in embedding space — generated with Nano Banana.

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LLM Architecture Series – Lesson 20 of 20. We have visited every component of the architecture. This lesson ties them together into a single mental model.

By walking through a full end to end example you can see how tokenization, embeddings, attention, MLPs, and the output layer cooperate to produce text.

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LLM Architecture Series – Lesson 19 of 20. You now understand the core architecture. Scaling is about what happens when we make models wider, deeper, and train them on more data.

Surprisingly, performance often follows smooth scaling laws, which lets practitioners predict the benefit of using larger models.

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LLM Architecture Series – Lesson 18 of 20. The output layer produces one logit per token in the vocabulary. Softmax converts these logits into a proper probability distribution.

These probabilities drive sampling strategies such as greedy decoding, top k sampling, and nucleus sampling.

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LLM Architecture Series – Lesson 17 of 20. After many transformer layers, we have a final hidden vector for each position. The output layer turns this into raw scores for every token in the vocabulary.

This linear layer is often called the language model head and is where most parameters of the model live.

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LLM Architecture Series – Lesson 16 of 20. A single transformer block is powerful, but modern LLMs use many of them in sequence.

Each additional layer can capture longer range patterns and refine the representations produced by earlier layers.

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LLM Architecture Series – Lesson 15 of 20. Now we bring all familiar components together into the standard transformer block.

Each block contains attention, MLP, layer norms, and residual paths wired in a specific order.

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LLM Architecture Series – Lesson 14 of 20. At this point attention and MLP layers are doing heavy work. Residual connections make sure information can flow easily through many layers.

By adding the input of a block back to its output, residual paths help gradients move during training and preserve useful signals.

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LLM Architecture Series – Lesson 13 of 20. After attention and projection we pass each position through a feed forward network.

This multilayer perceptron applies the same small neural network to every position independently, adding powerful non linear transformations.

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