Returns Distribution Explorer
See how the asset that you're looking to buy typically performs in terms of average returns and its distribution historically.
Try It Now ↗Simple apps for retail traders to visualise asset risks and returns more easily
See how the asset that you're looking to buy typically performs in terms of average returns and its distribution historically.
Try It Now ↗See how the asset that you're looking to buy typically performs across the weeks, months and years.
Try It Now ↗See how the asset that you're looking to buy typically performs vis-a-vis the overall market.
Try It Now ↗Simple apps for retail traders to backtest commonly adopted retail trading strategies
Thinking of buying the dip? See how that strategy would have performed if you adopted it in the past.
Try It Now ↗Thinking of dollar cost averaging? See how that strategy might perform versus buying it up front.
Try It Now ↗Wondering if you should continue to chase the rally? See how that strategy might play out.
Try It Now ↗Backtest simple rule-based strategies that aim to follow market trends
Backtest a basic moving average rule: buy when price is above the moving average and sell when it falls below.
Try It Now ↗Test a classic trend-following strategy using fast and slow moving average crossovers.
Try It Now ↗Explore a popular momentum-based trend strategy using the Moving Average Convergence Divergence indicator.
Try It Now ↗Backtest rule-based strategies that aim to profit from price reverting to its historical average
Test a classic mean reversion strategy using the Relative Strength Index (RSI) to buy oversold assets and sell overbought ones.
Try It Now ↗Backtest a volatility-based mean reversion strategy that trades price movements relative to Bollinger Bands.
Try It Now ↗Explore statistical mean reversion by trading extreme deviations from the historical mean using z-scores.
Try It Now ↗Design, test, and optimise multi-asset portfolios using intuitive backtesting and modern portfolio theory tools
Backtest custom portfolios with user-defined asset weights, rebalancing rules, and benchmarks to understand real-world performance over time.
Try It Now ↗Construct optimal portfolios using mean-variance optimisation to balance expected returns against risk based on historical data.
Try It Now ↗Use Principal Component Analysis (PCA) to understand hidden risk factors and build more diversified, factor-aware portfolios.
Stay Updated ↗Explore plausible futures, stress-test expectations, and experiment with data-driven prediction techniques — without mistaking uncertainty for certainty.
Simulate many plausible future paths for a backtested strategy using historical return distributions. Focuses on ranges of outcomes, drawdowns, and risk — not point forecasts.
Try It Now ↗Use machine learning classification to estimate whether the next interval return is more likely to be positive or negative, based on historical features and market conditions.
Stay Updated ↗Apply regression models such as Random Forests to predict the magnitude of next-interval returns. Designed for experimentation and learning, not trading signals.
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