Complete Trading Strategy Workflow with Bollinger Bands

This comprehensive example demonstrates a complete end-to-end trading workflow using the TradeTide library. We’ll build a Bollinger Bands-based trading strategy, backtest it on historical CAD/USD data, and analyze the results.

The workflow covers: - Market data loading and visualization - Technical indicator configuration (Bollinger Bands) - Strategy setup and signal generation - Position management and risk controls - Portfolio simulation and performance analysis

This example is perfect for understanding how all TradeTide components work together to create a professional trading system.

Import Required Libraries

We start by importing all necessary modules from TradeTide and standard libraries.

import matplotlib.pyplot as plt
import numpy as np
from TradeTide import (
    Strategy,
    Portfolio,
    PositionCollection,
    Market,
    Currency,
    days,
    hours,
    minutes,
)
from TradeTide.indicators import BollingerBands
from TradeTide import capital_management, exit_strategy

# Configure matplotlib for better plots
plt.style.use("seaborn-v0_8-darkgrid")
plt.rcParams["figure.figsize"] = (12, 8)
plt.rcParams["font.size"] = 10

Load Market Data

First, we load historical forex data for the CAD/USD currency pair. We’ll use 4 hours of data to demonstrate the strategy on recent market movements.

market = Market()

# Load 4 hours of CAD/USD data from the database
market.load_from_database(
    currency_0=Currency.CAD,
    currency_1=Currency.USD,
    time_span=4 * hours,
)

# Display basic market information
market.display()

Configure Bollinger Bands Indicator

We’ll use Bollinger Bands as our primary technical indicator. Bollinger Bands consist of a moving average with upper and lower bands that expand and contract based on market volatility.

# Configure Bollinger Bands with 3-minute window and 1.0 standard deviation multiplier
indicator = BollingerBands(
    window=3 * minutes,  # Moving average window
    multiplier=1.0,  # Standard deviation multiplier for bands
)

# Run the indicator on our market data
indicator.run(market)

Build Trading Strategy

Now we create a trading strategy that uses our Bollinger Bands indicator to generate buy and sell signals.

# Create strategy and add our indicator
strategy = Strategy()
strategy.add_indicator(indicator)

# Generate trading signals based on the strategy
trade_signals = strategy.get_trade_signal(market)
signal_count = np.count_nonzero(trade_signals)

print(f"Strategy generated {signal_count} trading signals")

Strategy generated 72 trading signals

Visualize Bollinger Bands and Market Data

Let’s plot the Bollinger Bands to visualize the indicator behavior and understand where trading signals are generated.

# Create the indicator plot (shows only ask prices for clarity)
figure = indicator.plot(show=False)

ax = figure.axes[0]

# Enhance the plot with additional information
ax.set_title("CAD/USD with Bollinger Bands Strategy", fontsize=14, fontweight="bold")
ax.set_xlabel("Time", fontsize=12)
ax.set_ylabel("Price", fontsize=12)
ax.grid(True, alpha=0.3)

# Add legend
ax.legend(loc="upper left", frameon=True, fancybox=True, shadow=True)

# Show the plot
plt.tight_layout()
# plt.show()

Configure Risk Management

Before opening positions, we need to set up our exit strategy and risk management parameters to control losses and secure profits.

# Set up static exit strategy with stop loss and take profit
risk_strategy = exit_strategy.Static(
    stop_loss=4,  # Stop loss at 4 pips
    take_profit=4,  # Take profit at 4 pips
    save_price_data=True,  # Save price data for analysis
)

Position Management

Create and manage trading positions based on our strategy signals.

# Create position collection with our market data and trading signals
position_collection = PositionCollection(
    market=market,
    trade_signal=trade_signals,
)

position_collection.plot()

# Open positions based on signals and apply exit strategy
position_collection.open_positions(exit_strategy=risk_strategy)

# Propagate positions through time to simulate trading
position_collection.propagate_positions()

total_positions = len(position_collection)
print(f"Opened {total_positions} positions during simulation")

Opened 72 positions during simulation

Capital Management Setup

Configure capital management rules to control position sizing and overall risk.

# Configure fixed lot capital management
capital_mgmt = capital_management.FixedLot(
    capital=1_000_000,  # Starting capital: $1M
    fixed_lot_size=10_000,  # Fixed position size: $10K
    max_capital_at_risk=100_000,  # Maximum capital at risk: $100K
    max_concurrent_positions=100,  # Maximum concurrent positions: 100
)

Portfolio Simulation

Run the complete portfolio simulation to see how our strategy performs.

# Create portfolio and run simulation
portfolio = Portfolio(position_collection=position_collection, debug_mode=False)
portfolio.simulate(capital_management=capital_mgmt)

Performance Analysis

Analyze the results of our trading strategy and display key performance metrics.

# Get comprehensive performance metrics
metrics = portfolio.get_metrics()

# Display all performance metrics
metrics.display()

Results Interpretation and Summary

Let’s interpret the results and provide insights about the strategy performance.

print("\n" + "=" * 60)
print("STRATEGY INSIGHTS AND INTERPRETATION")
print("=" * 60)

# Strategy summary
print(f"\nStrategy Summary:")
print(f"   - Indicator: Bollinger Bands (3-min window, 1.0 sigma)")
print(f"   - Risk Management: 4-pip stop loss & take profit")
print(f"   - Capital: $1M with $10K fixed lot sizes")
print(f"   - Time Period: 4 hours of CAD/USD data")
print(f"   - Total Positions: {total_positions}")

print(f"\nKey Takeaways:")
print(f"   - This example demonstrates a complete TradeTide workflow")
print(f"   - Bollinger Bands provide volatility-based trading signals")
print(f"   - Risk management is crucial for protecting capital")
print(f"   - Portfolio simulation enables strategy evaluation")

print(f"\nNext Steps:")
print(f"   - Experiment with different indicator parameters")
print(f"   - Try alternative exit strategies")
print(f"   - Test on different currency pairs and timeframes")
print(f"   - Implement additional technical indicators")

============================================================
STRATEGY INSIGHTS AND INTERPRETATION
============================================================

Strategy Summary:
   - Indicator: Bollinger Bands (3-min window, 1.0 sigma)
   - Risk Management: 4-pip stop loss & take profit
   - Capital: $1M with $10K fixed lot sizes
   - Time Period: 4 hours of CAD/USD data
   - Total Positions: 72

Key Takeaways:
   - This example demonstrates a complete TradeTide workflow
   - Bollinger Bands provide volatility-based trading signals
   - Risk management is crucial for protecting capital
   - Portfolio simulation enables strategy evaluation

Next Steps:
   - Experiment with different indicator parameters
   - Try alternative exit strategies
   - Test on different currency pairs and timeframes
   - Implement additional technical indicators

Conclusion

This example showcased a complete trading workflow using TradeTide. The modular design allows easy experimentation with different strategies, indicators, and risk management approaches.

For more advanced examples, explore:

• Multi-indicator strategies

• Dynamic exit strategies

• Portfolio optimization

• Walk-forward analysis