Free Stock Backtesting Tool – Test RSI, MACD, Bollinger Bands & More
Evaluate your trading strategies against historical market data with QuantStock's powerful backtesting platform. Test 17+ technical indicators including RSI, MACD, Moving Average crossovers, Bollinger Bands, Ichimoku Cloud, and Stochastic Oscillator. Analyze key performance metrics like Sharpe ratio, maximum drawdown, win rate, and profit factor to make data-driven trading decisions.
Performance Dashboard
Price Chart with Trading Signals
Strategy Indicators
Backtest Results
Returns
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| Annual Return | - |
| Max Drawdown | - |
| Sharpe Ratio | - |
Trade Statistics
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| Win Rate | - |
| Avg. Profit/Trade | - |
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| # | Entry Date | Exit Date | Entry Price | Exit Price | Position | P/L | P/L % |
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What is Stock Backtesting?
Backtesting is the process of evaluating a trading strategy by applying it to historical market data to see how it would have performed in the past. This simulation helps traders understand the potential profitability and risk of a strategy before committing real capital.
A well-executed backtest provides insights into key metrics like total returns, maximum drawdown, Sharpe ratio, and win rate. These metrics help traders compare different strategies objectively and identify approaches that align with their risk tolerance and investment goals.
Why Backtesting Matters
Professional traders and quantitative analysts rely on backtesting to validate their hypotheses before live trading. Without proper backtesting, traders risk deploying untested strategies that may result in significant losses. QuantStock's backtesting tool provides institutional-grade analytics accessible to retail traders, enabling data-driven decision making without expensive software subscriptions.
Technical Indicators for Backtesting
Technical indicators are mathematical calculations based on price, volume, or open interest that traders use to predict future price movements. QuantStock supports backtesting with 17+ popular indicators, each with customizable parameters:
RSI (Relative Strength Index)
Momentum oscillator measuring overbought (>70) and oversold (<30) conditions. Popular for mean reversion strategies.
Learn RSI Strategy →MACD
Trend-following momentum indicator showing relationship between two moving averages. Great for trend confirmation.
Learn MACD Strategy →Bollinger Bands
Volatility indicator with upper and lower bands that expand and contract. Used for breakout and mean reversion.
Learn Bollinger Bands →Moving Average Crossover
Classic trend strategy using SMA or EMA crossovers. Golden Cross (bullish) and Death Cross (bearish) signals.
Learn Moving Averages →Stochastic Oscillator
Momentum indicator comparing closing price to price range over time. Effective in ranging markets.
Learn Stochastic →Ichimoku Cloud
Comprehensive indicator showing support/resistance, trend direction, and momentum in one view.
Learn Ichimoku →Understanding Your Backtest Results
Interpreting backtest results correctly is crucial for making informed trading decisions. Here are the key metrics QuantStock calculates and what they mean:
Return Metrics
Total Return shows the overall percentage gain or loss of your strategy. While important, it should always be considered alongside risk metrics. CAGR (Compound Annual Growth Rate) normalizes returns over time, making it easier to compare strategies tested over different periods.
Risk Metrics
Maximum Drawdown measures the largest peak-to-trough decline, showing the worst-case scenario your portfolio would have experienced. Most professional traders target max drawdowns under 20%. The Sharpe Ratio measures risk-adjusted returns – a ratio above 1.0 is generally considered good, above 2.0 is very good, and above 3.0 is excellent.
Trade Statistics
Win Rate shows the percentage of profitable trades. Surprisingly, many successful strategies have win rates below 50% but compensate with larger average wins than losses. Profit Factor (gross profits / gross losses) above 1.5 typically indicates a robust strategy.
Common Backtesting Mistakes to Avoid
1. Overfitting (Curve Fitting)
The most dangerous mistake is optimizing parameters too closely to historical data. An overfitted strategy performs amazingly in backtests but fails in live trading. To avoid this: use out-of-sample testing, keep strategies simple with few parameters, and be skeptical of strategies that seem "too good to be true."
2. Survivorship Bias
Testing only on stocks that exist today ignores companies that went bankrupt or were delisted. This can significantly inflate backtest returns. Always be aware that historical data may not include failed companies.
3. Ignoring Transaction Costs
High-frequency strategies can appear profitable until you factor in commissions, spreads, and slippage. Always account for realistic transaction costs in your backtests.
4. Look-Ahead Bias
Using information that wouldn't have been available at the time of the trade decision invalidates results. QuantStock's engine is designed to prevent look-ahead bias by processing data chronologically.