DEMA Cross Strategy Backtest TSLA 20.98% Return With 60% Win Rate

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

The headline result is 20.98% Return With 60% Win Rate.

Strategy Idea

The strategy uses double exponential moving averages to reduce lag versus ordinary moving averages, then trades fast-versus-slow DEMA crossovers.

The logic can be summarized as:

1. Calculate a fast DEMA and a slow DEMA.
2. Use CrossOver to detect directional flips.
3. Enter long when fast DEMA crosses above slow DEMA.
4. Exit when the fast DEMA crosses back below the slow DEMA.
5. Keep the system compact and easy to compare against EMA-based variants.

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 two DEMA lines define the trading signal:

self.dema_fast = bt.indicators.DEMA(self.data_close, period=self.params.fast_dema)
self.dema_slow = bt.indicators.DEMA(self.data_close, period=self.params.slow_dema)
self.crossover = bt.indicators.CrossOver(self.dema_fast, self.dema_slow)

A bullish crossover creates a long setup:

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

The long exit is the opposite crossover:

elif self.position.size > 0:
    if self.crossover < 0:
        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 $12,097.54.

Drawdown vs Benchmark

The drawdown chart shows maximum strategy drawdown at 17.37%.

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, 165 flat strategy-equity days were removed from the histogram, leaving 85 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

DemaCrossStrategy 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.