Live Backtest Results
This backtest analyzes the performance of the 9/21 SMA crossover strategy on BTC/USDT over the 10 Minute 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.
ROI
-17.5%
Win Rate
24.8%
Max DD
17.6%
Sharpe
-12.30
Profit Factor
0.39
Total Trades
129
Backtest insights
The Moving Average crossover strategy generated a total return of -17.5%, indicating a net loss over the backtest period. The maximum drawdown of 17.6% suggests moderate risk exposure that requires active management. With a win rate of 24.8% across 129 trades, the strategy shows a low win rate, expected for crossover strategies that rely on large winners to offset frequent small losses and a statistically meaningful trade count.
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 10 Minute 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. At the 10 Minute level, the 9 and 21-period SMAs converge and diverge rapidly, generating 129 signals over the 1 month backtest window. The high signal frequency makes execution speed and transaction cost management critical, the theoretical edge visible in backtests can erode quickly in live trading with real spreads and slippage.
When It Works Best
At the 10 Minute level, this strategy performs best during high-liquidity sessions with sustained micro-trend momentum. When BTC/USDT makes strong directional moves within a session, particularly during the New York or London open, the 9-period SMA can stay cleanly above or below the 21-period SMA for long enough to produce a meaningful winning trade before a reversal occurs.
When It Performs Poorly
At the 10 Minute level, the strategy is highly vulnerable to the noise that dominates most short-term price action. The vast majority of intraday minutes involve BTC/USDT oscillating without meaningful directional commitment, causing the 9 and 21-period SMAs to cross repeatedly and generate losing trades in rapid succession. Transaction costs amplify these losses significantly across hundreds of trades per month.
Strengths
High signal frequency provides constant market participation during trending micro-sessions
Fully automated crossover logic is straightforward to implement in algorithmic execution
Objective, rules-based signals eliminate discretionary uncertainty in volatile short-term markets
Limitations
Transaction costs and slippage at this frequency can eliminate any edge, even in backtests
The vast majority of signals are false positives, low win rates require very high reward-to-risk on winning trades
Near-automated execution is essential; manual trading at this speed is prone to errors and emotional decisions
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 10 Minute timeframe?
Based on the backtest results above, the 9/21 SMA crossover strategy on BTC/USDT over the 10 Minute timeframe achieved a return of -17.5% with a maximum drawdown of 17.6% across 129 trades over 1 month. Results may vary depending on the market regime during any given period.
Is the Moving Average crossover strategy reliable for BTC/USDT on the 10 Minute timeframe?
On the 10 Minute timeframe, reliability is low without additional filters. The 24.8% win rate and 0.39 profit factor from this backtest reflect the challenge of using a lagging crossover at short intervals where noise dominates. Transaction costs compound quickly with 129 trades over 1 month. Additional filters or a longer timeframe tend to improve results significantly.
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 10 Minute timeframe?
On the 10 Minute timeframe, the 9/21 SMA combination generates very high signal frequency. Switching to EMAs can reduce lag slightly, but the fundamental challenge of noise at this speed remains. Many traders add a minimum crossover angle filter or a volatility gate to reduce false signals. CoinQuant lets you test these variations quickly to find what improves performance at this timeframe.
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