Live Backtest Results
This backtest analyzes the BTC Linear Regression strategy over the 4 hour timeframe. The tested logic is consistent across the page: A long entry triggers when BTC/USDT closes above the 20-period Linear Regression line on the selected timeframe. The position exits when price closes back below that same line, turning the regression line into a dynamic trend filter.
ROI
1117.3%
Win Rate
33.8%
Max DD
63.98%
Sharpe
0.83
Profit Factor
1.10
Total Trades
1640
Backtest insights
The Linear Regression strategy generated a total return of 1117.3% over the 4 hour timeframe. With a maximum drawdown of 63.98% and a win rate of 33.8% across 1640 trades, the result shows how this indicator rule reacted to BTC/USDT trend changes during the tested window. The same entry, exit, and timeframe rules are used across every metric on this page.
Performance may vary depending on market conditions. During trending periods, the strategy may behave differently compared to ranging markets, impacting both returns and drawdowns.
How the BTC Linear Regression Strategy Works
What It Is
The Linear Regression indicator fits a trend line to recent price action over a defined lookback window. This strategy tests a simple price crossover above and below the 20-period Linear Regression line. The page reports a real CoinQuant backtest on BTC/USDT 4 hour data.
How Signals Are Generated
A long entry triggers when BTC/USDT closes above the 20-period Linear Regression line on the selected timeframe. The position exits when price closes back below that same line, turning the regression line into a dynamic trend filter. This keeps the strategy auditable and repeatable inside CoinQuant.
When It Works Best
This strategy tends to work best when price respects a persistent directional path and pullbacks remain shallow enough for the regression line to act as a useful trend guide. The 4 hour timeframe captures a distinct market rhythm, so the same indicator can behave differently across horizons.
When It Performs Poorly
The strategy struggles when price oscillates around the regression line. Sideways markets can produce repeated crosses without meaningful trend continuation.
Strengths
Tests a clean trend-filter crossover
Adapts to recent price direction
Easy to audit across timeframes
Limitations
Can lag during sharp reversals
Whipsaws when price chops around the regression line
A 20-period lookback may not suit every asset regime
Why Use CoinQuant Instead of Manual Trading or Other Platforms
Choosing the right way to test and execute trading strategies is critical. Below is a comparison between CoinQuant, manual trading, and other platforms to highlight key differences in speed, accuracy, and usability.
CoinQuant is designed specifically for traders who want to validate strategies quickly and reliably without coding. Unlike manual trading or traditional platforms, it allows you to test multiple scenarios, analyze performance instantly, and iterate faster using real data.
Frequently asked questions
How does the Linear Regression strategy perform on BTC/USDT in the 4 hour timeframe?
In this backtest the Linear Regression strategy on the 4 hour timeframe generated a return of 1117.3% with a maximum drawdown of 63.98% and a win rate of 33.8% across 1640 trades. These results are based on historical backtest data and actual performance may vary.
What is the Linear Regression indicator?
The Linear Regression indicator fits a trend line to recent price action over a defined lookback window. This strategy tests a simple price crossover above and below the 20-period Linear Regression line.
Why is backtesting important for trading strategies?
Backtesting evaluates how a strategy would have performed on historical data before risking real capital. It reveals metrics like ROI, drawdown, and win rate that show whether a strategy has a genuine edge.
How can I test the Linear Regression strategy on CoinQuant?
Paste the exact strategy prompt from this page into CoinQuant, select BTC/USDT and the 4 hour timeframe, and CoinQuant generates a full backtest with performance metrics, no coding required.
What are the best settings for the Linear Regression strategy on the 4 hour timeframe?
Optimal settings depend on the indicator parameters, timeframe, market regime, and trading objective. The default tested here is the exact rule shown in the strategy prompt. CoinQuant lets you test parameter variations to find the best fit for the 4 hour timeframe.
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