Live Backtest Results
This backtest analyzes the performance of the 9/21 SMA crossover strategy on BTC/USDT over the 5 Day timeframe using historical market data. The results provide insight into how the strategy would have performed under real market conditions, including profitability, risk exposure, and consistency.
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ROI
-21.8%
Win Rate
0.0%
Max DD
23.0%
Sharpe
-1.56
Profit Factor
0.00
Total Trades
1
Backtest insights
The Moving Average crossover strategy generated a total return of -21.8%, indicating significant losses over the test period. The maximum drawdown of 23.0% suggests the strategy experienced a substantial decline in portfolio value, reflecting elevated downside risk. With a win rate of 0.0% across only 1 trade, the results are based on an extremely limited sample and should be interpreted with caution. A Sharpe ratio of -1.56 points to poor risk-adjusted performance, while a profit factor of 0.00 confirms that no profitable trades were recorded. Given the lack of trade frequency and negative performance metrics, additional testing across different market conditions and a larger dataset is needed to properly evaluate the strategy's viability.
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 Moving Average Strategy Works
What It Is
The Moving Average crossover strategy uses two simple moving averages, a fast 9-period SMA and a slow 21-period SMA, to identify trend direction and generate trade signals on BTC/USDT. When the fast SMA crosses above the slow SMA, it signals rising momentum and triggers a buy entry. When the fast SMA crosses back below the slow SMA, it signals weakening momentum and triggers an exit. Applied to the 5 Day timeframe, each crossover reflects a meaningful shift in directional momentum at that time scale.
How Signals Are Generated
In this strategy, signals are generated when the 9-period SMA crosses the 21-period SMA on BTC/USDT. A buy signal fires when the 9-period SMA crosses above the 21-period SMA, indicating that short-term momentum is accelerating relative to the medium-term trend. An exit signal fires when the 9-period SMA crosses back below the 21-period SMA, signalling that the momentum advantage has reversed. On the 5 Day timeframe, each crossover reflects a macro-level momentum shift spanning days or weeks of price action. With 10 trades over the 1 year backtest, signals are infrequent but each one represents a significant directional commitment. Capital is deployed deliberately and held through entire trend phases.
When It Works Best
This strategy performs best when BTC/USDT is in a sustained directional trend on the 5 Day timeframe. Bull market phases, where price consistently makes higher highs and the fast SMA stays clearly above the slow SMA, generate the cleanest signals. Post-consolidation breakouts and trend resumptions after major market structure events are particularly favourable setups.
When It Performs Poorly
The strategy underperforms during choppy, range-bound markets on the 5 Day timeframe, where BTC/USDT oscillates without establishing a clear trend. In these conditions, the 9 and 21-period SMAs repeatedly cross each other in quick succession, generating a series of small losses known as whipsaws. Sharp news-driven reversals can also cut profitable trends short, turning winning positions into losses before the exit signal fires.
Strengths
Captures sustained directional momentum that spans multiple trading days
Reduces overtrading by filtering out short-term noise with longer-period SMAs
Fully systematic signal generation removes emotional bias from entry and exit decisions
Limitations
SMAs are lagging indicators, crossovers frequently occur after much of the directional move has already happened
Whipsaw losses in choppy markets can accumulate quickly and erode trend-following gains
Low win rates are typical; success depends on a few large winners more than offsetting many small losses
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 Moving Average strategy perform on BTC/USDT in the 5 Day timeframe?
Max Drawdown: 23.0%
Is the Moving Average crossover strategy reliable for BTC/USDT on the 5 Day timeframe?
The 9/21 SMA crossover can be effective on longer timeframes when BTC/USDT enters sustained macro trend phases. On the 5 Day timeframe, each signal represents a major momentum commitment, but the low trade count means statistical reliability is limited. A few whipsaw losses can significantly skew the overall result, so risk management per trade is critical.
Why is backtesting important for trading strategies?
Backtesting allows traders to evaluate how a strategy would have performed using historical data. It helps identify strengths, weaknesses, and risk levels before applying the strategy in real markets, reducing the likelihood of unexpected losses.
How can I test the Moving Average strategy on CoinQuant?
You can use CoinQuant to build and backtest the Moving Average crossover strategy without any coding. Simply type the prompt shown below into the CoinQuant chat box and the platform will parse your instruction, generate the strategy logic, and run the full backtest automatically.
What are the best settings for the Moving Average strategy on the 5 Day timeframe?
On the 5 Day timeframe, the 9/21 SMA pairing is a widely used starting point. Traders sometimes widen the gap, using a 9/50 or 20/50 combination, to reduce whipsaws and focus only on major trend reversals. Using exponential moving averages (EMAs) instead of SMAs can also improve responsiveness. CoinQuant lets you test multiple configurations and compare results without writing code.
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