EMA Cross Strategy Backtest AMZN 19.99% Return With 7.38% Max Drawdown

Get the full strategy library: This article uses one strategy from the Mega Backtrader Strategy Pack, a package of 500+ Backtrader-ready Python trading strategies with batch backtesting, dashboards, metrics, charts, and documentation.

Download the package here: Mega Backtrader Strategy Pack

If you want to test complete Backtrader strategies without building every idea from scratch, the package gives you strategy code, batch runners, metrics, charts, and publishable reports in one workflow.

This article walks through EmaCrossStrategy, one of the strategies included in the Mega Backtrader Strategy Pack. The strategy was tested on AMZN daily candles from 2025-06-20 to 2026-06-18, with SPY used as the benchmark.

The headline result is 19.99% Return With 7.38% Max Drawdown.

Strategy Idea

The strategy uses a fast EMA and slow EMA crossover, then manages exits with both opposite crossover signals and a trailing stop.

The logic can be summarized as:

1. Calculate fast and slow exponential moving averages.
2. Use a Backtrader CrossOver indicator to detect turns.
3. Enter when the fast EMA crosses above the slow EMA.
4. Exit when the opposite crossover appears or the trailing stop is hit.
5. Use most available cash for position sizing in the original strategy code.

The package batch run used the default long-only execution mode, so short-style sell calls are handled as exits unless shorting is explicitly enabled.

Code Walkthrough

The crossover is built from a 7-period and 30-period EMA:

self.ema_fast = bt.indicators.EMA(self.data_close, period=self.params.fast_ema)
self.ema_slow = bt.indicators.EMA(self.data_close, period=self.params.slow_ema)
self.crossover = bt.indicators.CrossOver(self.ema_fast, self.ema_slow)

A bullish crossover creates the long signal:

if self.crossover > 0:
    cash = self.broker.get_cash()
    size = (cash * self.params.order_percentage) / current_price
    self.order = self.buy(size=size)

The trailing stop follows the highest price after entry:

trailing_stop_price = self.highest_price * (1 - self.params.trailing_stop_pct)
if current_price <= trailing_stop_price:
    self.order = self.sell(size=self.position.size)

The important point is that the strategy is a complete Backtrader class, not just a loose signal snippet. It defines indicators, position rules, exit behavior, and order handling in one reusable strategy module.

Backtest Setup

Results

This run ended with 0 open trade(s). Closed-trade statistics are based on completed trades, while final portfolio value is the account value at the final bar.

Equity Curve vs Benchmark

The equity curve shows the strategy path from $10,000.00 to $11,999.39.

Drawdown vs Benchmark

The drawdown chart shows maximum strategy drawdown at 7.38%.

Rolling Return vs Benchmark

The rolling return chart shows how performance developed across the test window.

Daily Return Distribution

The daily return distribution excludes zero-return strategy days. In this run, 208 flat strategy-equity days were removed from the histogram, leaving 42 non-zero strategy return days plotted.

What This Shows

This example shows how the Mega Backtrader Strategy Pack can turn a strategy idea into a full research artifact: code, batch execution, metrics, charts, and a publishable write-up.

For strategy research, this matters because one idea is rarely enough. You want to compare trend, momentum, mean reversion, volatility, volume, machine learning, and ensemble approaches across multiple assets and time windows. A large strategy library gives you that workflow immediately.

Get the Complete Strategy Library

EmaCrossStrategy is one of 500+ Backtrader-ready strategies included in the Mega Backtrader Strategy Pack.

Get the full package here: Mega Backtrader Strategy Pack

The package includes:

• 500+ Python strategy modules in the strategies/ package.
• Fast batch backtesting with multiprocessing.
• Per-strategy metrics, equity curves, drawdown plots, and CSV outputs.
• A browsable HTML strategy catalog.
• A Markdown strategy package manual.
• Windows and shell batch runners for automated research.

If you want to test more strategy ideas faster, this package gives you the code and the research workflow.

Get the Mega Backtrader Strategy Pack

Disclaimer

This article is for research and educational use only. Backtest results are not financial advice, investment advice, or a guarantee of future performance. Always validate strategy logic, data quality, execution assumptions, costs, slippage, and risk before using any trading system.