I Tried the Ichimoku Cloud Trading Strategy and This Is What Happened

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Why Ichimoku Cloud?

If you’re new to trading, one problem is knowing when to enter or exit a position.

The Ichimoku Cloud simplifies that decision by combining multiple indicators into one clear visual system.

The Ichimoku Cloud is one of the most fascinating indicators I’ve explored. Unlike standard indicators, it gives a full picture of trend, momentum, and potential support/resistance zones, all in one glance.

In this article, we’re going to build the Ichimoku Cloud strategy from scratch using Python. We’ll go through:

• Pulling historical price data

• Calculating the Ichimoku components

• Visualizing the cloud and signals

• Backtesting a simple strategy

• Evaluating performance with equity curves and drawdowns

Everything is explained step by step so that even if you’ve never coded before, you’ll follow along.

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1. Install and Import Required Libraries

Before we start, we need a few libraries:

• yfinance to download historical stock data

• pandas for calculations

• matplotlib for visualizations

• tabulate to display backtest stats nicely

Here’s how we set that up:

%pip install yfinance matplotlib tabulate -q

Next, import the libraries:

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
from tabulate import tabulate

2. Download Historical Price Data

We need historical stock prices to test the strategy. Here we use Apple (AAPL) as an example:

# Download historical stock data
symbol = "AAPL"  # You can change to BTC-USD, TSLA, etc.
data = yf.download(symbol, start="2020-01-01", end="2024-12-31")

# Select the desired columns (first level of MultiIndex)
data.columns = data.columns.get_level_values(0)

data.head()

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3. Visualize Closing Price

Before adding the indicator, let’s see how the stock price moved:

# Plot closing price only
plt.figure(figsize=(14,6))
plt.plot(data.index, data['Close'], label=f"{symbol} Closing Price", color="black", linewidth=1.5)
plt.title(f"{symbol} Closing Price (2020-2024)")
plt.xlabel("Date")
plt.ylabel("Price")
plt.legend()
plt.savefig(f"figures/{symbol}_closing_price.png", dpi=300)
plt.show()

4. Calculate Ichimoku Components

The Ichimoku Cloud is made up of five lines:

1. Tenkan-sen

2. Kijun-sen

3. Senkou Span A

4. Senkou Span B

5. Chikou Span

Here’s how we calculate them:

# Ichimoku settings (standard: 9, 26, 52)
high = data['High']
low = data['Low']
close = data['Close']

# Tenkan-sen (Conversion Line): (9-period high + 9-period low) / 2
data['tenkan'] = (high.rolling(9).max() + low.rolling(9).min()) / 2

# Kijun-sen (Base Line): (26-period high + 26-period low) / 2
data['kijun'] = (high.rolling(26).max() + low.rolling(26).min()) / 2

# Senkou Span A (Leading Span A): (tenkan + kijun) / 2, shifted 26 periods forward
data['senkou_a'] = ((data['tenkan'] + data['kijun']) / 2).shift(26)

# Senkou Span B (Leading Span B): (52-period high + 52-period low) / 2, shifted 26 periods forward
data['senkou_b'] = ((high.rolling(52).max() + low.rolling(52).min()) / 2).shift(26)

# Chikou Span (Lagging Span): closing price shifted -26 periods
data['chikou'] = close.shift(-26)

• Tenkan-sen: short-term trend

• Kijun-sen: medium-term trend

• Senkou A & B: define the cloud (Kumo)

• Chikou: confirms momentum

This calculation sets up the foundation for both visualization and strategy signals.

5. Visualize the Ichimoku Cloud

Now let’s overlay the lines and cloud on the price chart:

plt.figure(figsize=(14,8))
plt.plot(data.index, data['Close'], label="Close", color='black', linewidth=1)

# Tenkan & Kijun
plt.plot(data.index, data['tenkan'], label="Tenkan (Conversion)", color='blue', linewidth=1.2)
plt.plot(data.index, data['kijun'], label="Kijun (Base)", color='red', linewidth=1.2)

# Cloud area
plt.fill_between(
    data.index, data['senkou_a'], data['senkou_b'],
    where=data['senkou_a'] >= data['senkou_b'],
    color='lightgreen', alpha=0.4
)
plt.fill_between(
    data.index, data['senkou_a'], data['senkou_b'],
    where=data['senkou_a'] < data['senkou_b'],
    color='lightcoral', alpha=0.4
)

# Chikou
plt.plot(data.index, data['chikou'], label="Chikou (Lagging)", color='green', linestyle='dotted')

plt.title(f"{symbol} Ichimoku Cloud")
plt.legend(loc="upper left")
plt.savefig(f"figures/{symbol}_ichimoku_cloud.png", dpi=300)
plt.show()

• Green cloud = bullish

• Red cloud = bearish

• Tenkan/Kijun crossovers hint at potential trades

• Chikou shows momentum

6. Define a Simple Trading Strategy

We’ll generate buy signals when the price closes above the cloud and Tenkan crosses above Kijun:

# Strategy: Buy when price closes above cloud and Tenkan crosses above Kijun
data['above_cloud'] = data['Close'] > data[['senkou_a', 'senkou_b']].max(axis=1)
data['tenkan_cross'] = (data['tenkan'] > data['kijun']) & (data['tenkan'].shift(1) <= data['kijun'].shift(1))

data['signal'] = 0
data.loc[data['above_cloud'] & data['tenkan_cross'], 'signal'] = 1  # Buy signal

7. Visualize Trade Signals

We identify trades as buy → sell pairs and plot them with the cloud:

# Find trades: buy → sell pairs
buy_points = []
sell_points = []
position = 0  # 0 = flat, 1 = long

for i in range(len(data)):
    if data['signal'].iloc[i] == 1 and position == 0:
        buy_points.append((data.index[i], data['Close'].iloc[i]))
        position = 1
    elif position == 1 and data['Close'].iloc[i] < data[['senkou_a','senkou_b']].min(axis=1).iloc[i]:
        sell_points.append((data.index[i], data['Close'].iloc[i]))
        position = 0

# Convert to DataFrames for plotting
buy_df = pd.DataFrame(buy_points, columns=["Date","Price"]).set_index("Date")
sell_df = pd.DataFrame(sell_points, columns=["Date","Price"]).set_index("Date")

# Plot with clean trade-based signals
plt.figure(figsize=(14,8))
plt.plot(data.index, data['Close'], color='black', linewidth=1, label="Close")

plt.scatter(buy_df.index, buy_df['Price'], marker="^", color="green", s=100, label="Buy Signal")
plt.scatter(sell_df.index, sell_df['Price'], marker="v", color="red", s=100, label="Sell Signal")

# Cloud
plt.fill_between(
    data.index, data['senkou_a'], data['senkou_b'],
    where=data['senkou_a'] >= data['senkou_b'],
    color='lightgreen', alpha=0.4
)
plt.fill_between(
    data.index, data['senkou_a'], data['senkou_b'],
    where=data['senkou_a'] < data['senkou_b'],
    color='lightcoral', alpha=0.4
)

plt.title(f"{symbol} Ichimoku Cloud with Trade-Based Buy & Sell Signals")
plt.legend()
plt.savefig(f"figures/{symbol}_trade_signals.png", dpi=300)
plt.show()

• Green arrows = buy

• Red arrows = sell

• Only one sell per buy

• Cloud helps visualize where the price is strong or weak

8. Backtest the Strategy

We simulate a portfolio starting with $10,000:

# Backtest parameters
initial_balance = 10000
position = 0
balance = initial_balance
equity_curve = []

for i in range(len(data)):
    # Buy condition
    if data['signal'].iloc[i] == 1 and position == 0:
        position = balance / data['Close'].iloc[i]
        balance = 0
    
    # Sell condition: price closes below the cloud
    elif position > 0 and data['Close'].iloc[i] < data[['senkou_a', 'senkou_b']].min(axis=1).iloc[i]:
        balance = position * data['Close'].iloc[i]
        position = 0
    
    # Track portfolio value
    equity_curve.append(balance + position * data['Close'].iloc[i])

data['equity'] = equity_curve

# Plot equity curve
plt.figure(figsize=(14,6))
plt.plot(data.index, data['equity'], label="Equity Curve", color="blue")
plt.title("Equity Curve of Ichimoku Strategy")
plt.legend()
plt.savefig(f"figures/{symbol}_equity_curve.png", dpi=300)
plt.show()

9. Evaluate Backtest Performance

Now that we have simulated the strategy, let’s calculate key performance metrics to see how it actually performed:

final_balance = data['equity'].iloc[-1]
total_return = (final_balance - initial_balance) / initial_balance * 100
max_equity = data['equity'].cummax()
drawdown = (data['equity'] - max_equity) / max_equity
max_drawdown = drawdown.min() * 100

stats = [
    ["Initial Balance", f"${initial_balance:,.2f}"],
    ["Final Balance", f"${final_balance:,.2f}"],
    ["Total Return", f"{total_return:.2f}%"],
    ["Max Drawdown", f"{max_drawdown:.2f}%"],
]

print(tabulate(stats, headers=["Metric", "Value"], tablefmt="github"))

╭─────────────────┬────────────╮
│ Metric          │ Value      │
├─────────────────┼────────────┤
│ Initial Balance │ $10,000.00 │
│ Final Balance   │ $13,177.42 │
│ Total Return    │ 31.77%     │
│ Max Drawdown    │ -32.36%    │
╰─────────────────┴────────────╯

Interpretation of Results

1. Initial vs. Final Balance: Starting with $10,000, the strategy grew the portfolio to $13,177, giving a total return of 31.77% over the 5-year period. That’s a solid gain for a rule-based, unoptimized strategy.

2. Max Drawdown: The maximum drawdown of -32.36% tells us the largest peak-to-trough loss during this period. Drawdowns are important because they show the risk of losing money at the worst point, even if the final return looks good.

3. Equity Curve Observation: By looking at the equity curve, you can see that the strategy captures upward trends but also experiences significant drops during downtrends. This is normal for trend-following strategies like Ichimoku.

10. Visualize Drawdowns

To get a better sense of the risk periods, we can visualize the drawdowns over time:

# Calculate drawdown
max_equity = data['equity'].cummax()              # Running peak
drawdown = (data['equity'] - max_equity) / max_equity  # % drop from peak

# Plot drawdown
plt.figure(figsize=(14,6))
plt.fill_between(data.index, drawdown, 0, color='red', alpha=0.4)
plt.title(f"{symbol} Drawdown Over Time")
plt.ylabel("Drawdown (%)")
plt.xlabel("Date")
plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f"{y*100:.0f}%"))
plt.savefig(f"figures/{symbol}_drawdown.png", dpi=300)
plt.show()

11. Highlight Maximum Drawdown on Equity Curve

For an even clearer view, we can mark exactly where the largest loss occurred:

# Calculate running peak and drawdown
peak = data['equity'].cummax()
drawdown = (data['equity'] - peak) / peak

# Find max drawdown
max_dd_idx = drawdown.idxmin()               # Date of max drawdown
peak_before_dd_idx = data['equity'][:max_dd_idx].idxmax()  # Peak before drawdown
max_dd_value = drawdown.min() * 100

# Plot equity curve
plt.figure(figsize=(14,6))
plt.plot(data.index, data['equity'], label="Equity Curve", color="blue")

# Highlight peak before max drawdown
plt.scatter(peak_before_dd_idx, data['equity'][peak_before_dd_idx], color="green", s=100, label="Peak before Max DD")

# Highlight trough of max drawdown
plt.scatter(max_dd_idx, data['equity'][max_dd_idx], color="red", s=100, label=f"Trough of Max DD ({max_dd_value:.2f}%)")

# Shade max drawdown area
plt.fill_between(data.index, data['equity'], peak, where=(data.index>=peak_before_dd_idx)&(data.index<=max_dd_idx), color='red', alpha=0.2)

plt.title(f"{symbol} Equity Curve with Max Drawdown Highlighted")
plt.xlabel("Date")
plt.ylabel("Equity ($)")
plt.legend()
plt.savefig(f"figures/{symbol}_max_drawdown.png", dpi=300)
plt.show()

Possible Improvements and Next Steps

The strategy we implemented is simple and unoptimized, which means there’s room to improve:

1. Adjusting Parameters: The standard Ichimoku settings (9, 26, 52) might not be optimal for every stock or market. Tweaking these values could improve returns or reduce drawdowns.

2. Optimization Methods: Techniques like Bayesian Optimization or grid search can systematically test parameter combinations and identify the most effective settings for a given market.

3. Risk Management Enhancements: Position sizing, Stop-losses and Trailing stops can help reduce drawdowns without sacrificing gains.

4. Combining with Other Indicators: Using Ichimoku in combination with momentum indicators or volume analysis could filter out false signals.

What’s Next

In this guide, you learned:

• How to calculate and visualize the Ichimoku Cloud in Python

• How to generate buy and sell signals based on cloud and Tenkan/Kijun crosses

• How to backtest a strategy with equity curves and drawdowns

• How to interpret the results and understand the risk-reward tradeoff

Even with a simple, unoptimized setup, the strategy gave 31.77% returns, showing the power of a structured approach to trading.