From research paper to real strategy results
We continuously research, build, and validate systematic trading ideas with robust testing, then share transparent results so everyone can access evidence-based quant insights, not just institutions. Backtested quantitative trading strategies across global equity indices, options analysis on US index ETFs, portfolio optimization, sentiment tracking, and market regime detection - all with live data updated daily.
What's Here
Quantitative strategies, options analysis, portfolio optimization, and market researchmore| Strategy Library | 70+ technical indicator-based strategies backtested across 21 global equity indices with 5 years of daily data. Each strategy has a dedicated page with equity curves, performance metrics, parameter optimization, and Python implementation code. |
| Options Strategies | 18 options strategies — from cash-secured puts and covered calls to iron condors, jade lizards, and synthetic longs — backtested on SPY, QQQ, IWM, and DIA using live IV surfaces and Black-Scholes pricing. Each strategy shows per-symbol performance, equity curves, and implementation code. |
| Portfolio Optimization | 30 systematic portfolio strategies built on S&P 500 stocks using factor signals (momentum, volatility, quality, trend). Skfolio-powered optimization comparing Max Sharpe, Min Variance, Risk Parity, HRP, and more across US equity and global indices. |
| Parameter Optimization | Grid search over each strategy's key parameters, optimizing for Sharpe ratio, Sortino, Calmar, win rate, and profit factor. Results visualized as line charts (1-parameter) or scatter heatmaps (2-parameter) showing the full performance surface. |
| Market Analysis | Relative Rotation Graphs for US equity and sector ETFs vs S&P 500, correlation matrix across all strategies, stock sentiment tracker using VADER on financial news headlines, and market regime detection using GMM and hidden Markov models. |
| Project Research | Adaptive portfolio strategies (OLPS algorithms), K-means clustering portfolio construction, OLPS comparison, and market regime detection — each with interactive visualizations, methodology documentation, and source code. |
Top Performers
Best risk-adjusted returns across all strategiesData as of 2026-04-10
Bitcoin Execution RL
DQN agent for optimal Bitcoin trade execution using Binance orderbook dataOptimal Bitcoin Execution with DQN Agent
Deep Q-Network trained on Binance BTCUSDT orderbook snapshots for minimum-cost execution.
Yield Curve Intelligence
Treasury-curve inversion diagnostics, Nelson-Siegel factors, and macro risk contextLive monitoring widget from the full Yield Curve project.
View Full ReportRisk reports & stop / take-profit analysis
How diversified equity and options backtests look in risk–return space, plus a controlled experiment on stops and profit targetsmoreOverview
The still images summarize many backtests at once (equity indices and listed options on ETFs). The interactive block at the bottom is a teaching exercise: the same basket of simple strategies is run again on one index with different stop-loss and take-profit settings, so you can see how averages move when you change rules.
Equity index strategies
Classic cross-sectional views: Sharpe versus drawdown, and sample equity and underwater paths for strong names. Each card explains the axes and what to look for when you compare dots or lines.
Each dot is one backtested strategy (often on a major index). Together they show how risk and reward line up across many rules in one glance.
Reading the chart
- Horizontal axis: maximum drawdown (%) — worst peak-to-trough loss in the sample.
- Vertical axis: Sharpe ratio — return per unit of volatility; higher usually means a smoother payoff per unit of risk.
- The upper-left corner (mild drawdown, higher Sharpe) is where you look first; extreme dots are worth opening to see which symbol and rule produced them.
Top panel: how equity evolves through time for a few standout runs. Bottom panel: how far under the running peak each path fell (underwater chart).
Reading the chart
- Smoother equity lines usually mean fewer violent swings; jagged lines can still end well but are harder to hold.
- Drawdown panel: both depth and how long the curve stays underwater matter for real-world discipline.
- Use the legend to match a line to a strategy and market; follow the link from the backtest section of the site for full metrics.
Options on US index ETFs
These charts come from the same options backtest study (multiple strategies on ETFs such as SPY and QQQ). They emphasize risk–return, how trades close, and the path of each trade through MAE and MFE — core concepts for any vol or options course.
Options-style backtests on major US index ETFs. Position color by total return so you can separate high-Sharpe structures that also made money from those that were “safe” but flat.
Reading the chart
- Same axes as the equity scatter: drawdown (%) vs Sharpe. Options books often spread wider than delta-one equity rules.
- Use color (return %) to compare two dots with similar Sharpe — one may be capturing more absolute P&L.
- These are model outputs (vol surface, assumptions, friction). Treat them as teaching examples, not live trade signals.
For a handful of strongest Sharpe names, bars show realized return (%) next to win rate (%). The two do not have to move together.
Reading the chart
- One bar group is return (%), the other is win rate (% of trades).
- Premium-selling styles often show high win rate with moderate return; long-vol or lottery-ticket styles can show the opposite.
- Read the tick labels as underlying ticker plus strategy nickname, then open that strategy’s page for full context.
Counts trades by how the position was closed — for example stop, profit target, expiry, or roll. Tells you what actually drives turnover in the simulation.
Reading the chart
- Tall single bar: one exit type dominates; your read of Sharpe and drawdown should reference that mechanism.
- Many stop exits: risk geometry and gap risk matter a lot for interpreting the equity curve.
- Many expiry exits: path inside the trade matters less than final settlement — check holding period in the detailed report.
MAE = worst unrealized loss versus entry while the trade was open. MFE = best unrealized profit before exit. Each point is one completed trade.
Reading the chart
- Horizontal: MAE (%), vertical: MFE (%). Points toward the upper-right had room to move both ways intratrade.
- Color shows realized P&L (%). Clusters of green with low MFE can mean winners were taken quickly.
- Many reds with very negative MAE can mean stops were wide or the structure bled before exit rules fired.
Stop / target experiment (static figures)
These match the interactive charts below: one symbol, one horizon, many strategies, several stop and take-profit configurations. Use the PNGs for slides or print; use the live charts to hover exact numbers.
Compares several risk settings applied to the same basket of simple strategies on one index. Bars summarize averages across that basket.
Reading the chart
- This PNG may rescale series so all three fit visually; use the interactive chart on this page for raw numbers on one scale.
- Read across: baseline vs stop-only vs target-only vs combined tells you which rule family moves the average the most.
Only the stop under entry moves; profit targets are turned off for this experiment. The left end is “no fixed stop” in this setup.
Reading the chart
- Upward slope: the average strategy Sharpe rises when the stop is wider (often fewer whipsaws).
- Downward slope: tighter stops hurt the average Sharpe in this sample — cutting loss also cuts good trades.
Only the profit target above entry moves; stops are off for this sweep. The left end is “no fixed target” here.
Reading the chart
- Larger targets may help trend rules ride longer or hurt mean-reversion rules that mean-revert quickly.
- A flat line means this basket is insensitive to target choice in the tested range; a steep line means target choice matters.
For each stop setting, height is the average of each strategy’s worst drawdown (%). Lower is shallower on average.
Reading the chart
- Tighter stops do not automatically produce smaller average drawdown — churn can create new drawdown episodes.
- Compare to the Sharpe line chart for the same sweep: you want joint insight, not one number alone.
Same drawdown idea as the stop-only chart, but only profit targets change. Baseline is the first bar.
Reading the chart
- Compare bar heights across 5%, 10%, 15% targets vs baseline — sometimes wider targets smooth the path.
- Pair with the take-profit vs Sharpe line to reason about risk and return together.
Interactive lab: stops and targets
Experiment design: fix 71 textbook-style strategies, one price series (^GSPC, 5y of daily data), then change only the stop-loss and/or take-profit rule. Every plotted value is an unweighted average across those strategies — a classroom-friendly summary, not an optimized portfolio. Last computed: Apr 10, 2026, 7:35 AM.
Compare scenarios: average Sharpe, return, and drawdown
- Avg Sharpe
- Avg return (%)
- Avg max DD (%)
Important: Sharpe (no units), return (%), and drawdown (%) share one vertical axis for compact display — compare blue bars to blue bars across scenarios, green to green, red to red. Do not compare blue height to green height as if they were the same quantity.
Stop-loss only: how average Sharpe moves
Y-axis: simple average of each strategy’s Sharpe at that stop. X-axis: stop width as a percent below entry (for example 5 means exit if price drops 5% below where you bought).
Take-profit only: how average Sharpe moves
Y-axis: same averaging idea as the stop chart. X-axis: target as a percent above entry — a 10 means lock gains if price rises 10% above entry, under the rules of the simulator.
Stop-loss only: average depth of drawdown
Shorter bars mean a shallower average drawdown in this basket. Averages are unweighted — each strategy counts the same, which is fine for teaching but not the same as a dollar-weighted book.
Take-profit only: average depth of drawdown
Read next to the take-profit vs Sharpe line: a target that lifts Sharpe might still change how deep the typical path dips.
Per-strategy SL/TP surface
Select a strategy to inspect its individual response surface instead of only basket averages. The table shows Sharpe by stop-loss (rows) and take-profit (columns), and the two curves isolate one dimension at a time for that selected strategy.
Generated Friday, Apr 10, 2026
| # | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| 1 | Chaikin Oscillator | Chaikin Oscillator | 0.97 | +63.1% | -7.2% | +52.8% | 36 | 3% | 5% |
| 2 | Bollinger Bands | Bollinger Bands | 0.84 | +69.7% | -12.8% | +78.6% | 14 | 7% | 10% |
| 3 | Z-Score Mean Reversion | Z-Score Mean Reversion | 0.84 | +69.7% | -12.8% | +78.6% | 14 | 7% | 10% |
| 4 | Stochastic RSI | Stochastic Rsi | 0.82 | +65.7% | -9.3% | +69.2% | 39 | 5% | 10% |
| 5 | Detrended Price Oscillator | Dpo | 0.81 | +44.6% | -8.1% | +41.7% | 24 | 0% | 5% |
Showing 1 to 5 of 71 strategies
| # | |||||||
|---|---|---|---|---|---|---|---|
| 1 | Reverse Iron Butterfly | Volatility | 0.84 | +1964.2% | +131.5% | +72.4% | 4 |
| 2 | Long Straddle | Volatility | 0.73 | +2541.4% | +131.7% | +72.4% | 4 |
| 3 | Long Straddles | Volatility | 0.73 | +2541.4% | +131.7% | +72.4% | 4 |
| 4 | Bull Call Spread | Spread | 0.65 | +4200.8% | +407.9% | +52.6% | 4 |
| 5 | Long Call Spread | Spread | 0.65 | +4200.8% | +407.9% | +52.6% | 4 |
| 6 | Reverse Iron Condor | Volatility | 0.65 | +16227.0% | +400.0% | +46.1% | 4 |
| 7 | Protective Put | Hedge | 0.63 | +60.3% | +13.5% | +72.4% | 4 |
| 8 | Short Put Calendar | Volatility | 0.57 | +1226.1% | +196.1% | +76.3% | 4 |
Portfolio Optimization
30 systematic strategies using 36 S&P 500 stocks
Risk-Return Profile
Top Strategies by Sharpe Ratio
Data as of Friday, Apr 10, 2026
| Rank | ||||||
|---|---|---|---|---|---|---|
| 1 | High Conviction Top 3 | Aggressive | 80.4% | 15.6% | 3.86 | -6.8% |
| 2 | Quality Composite Score | Quality | 62.4% | 13.9% | 3.55 | -4.0% |
| 3 | Golden Cross Portfolio | Trend | 67.4% | 15.3% | 3.45 | -4.6% |
| 4 | Above SMA50 + Momentum | Trend | 64.1% | 14.9% | 3.39 | -3.9% |
| 5 | 12-Month Momentum Top 10 | Momentum | 84.5% | 19.2% | 3.29 | -7.5% |
| 1. | Signal Generation Framework | Modular framework for generating trading signals from multiple data sources. Supports technical indicators, fundamental analysis, machine learning models, and custom signal generators. Includes signal validation, filtering, and combination logic to create robust entry and exit signals. |
| 2. | Execution Engine | High-performance order execution framework with support for multiple broker APIs and exchange connections. Features include order routing, slippage management, fill simulation, and real-time position tracking. Designed for both backtesting and live trading environments. |
| 3. | Risk Management System | Comprehensive risk management framework with position sizing, portfolio-level risk limits, drawdown controls, and real-time risk monitoring. Implements VaR calculations, position concentration limits, and dynamic risk adjustment based on market conditions and strategy performance. |
| 4. | Portfolio Management | Advanced portfolio construction and optimization framework. Supports multiple optimization objectives including Sharpe ratio maximization, risk parity, minimum variance, and custom utility functions. Includes rebalancing logic, transaction cost modeling, and constraint handling. |
| 5. | Data Pipeline & Processing | Scalable data ingestion and processing framework for market data, fundamental data, and alternative data sources. Features real-time data streaming, historical data management, data quality checks, and normalization. Supports multiple data formats and timeframes. |
| 6. | Backtesting Infrastructure | Robust backtesting framework with realistic market simulation, including bid-ask spreads, market impact, and partial fills. Supports walk-forward analysis, Monte Carlo simulation, and out-of-sample testing. Provides detailed performance metrics and attribution analysis. |
| 7. | Strategy Orchestration | Framework for managing multiple strategies simultaneously with resource allocation, priority queuing, and conflict resolution. Includes strategy lifecycle management, performance monitoring, and automated strategy deployment and retirement mechanisms. |
| 8. | Monitoring & Alerting | Real-time monitoring framework for system health, strategy performance, and market conditions. Features customizable alerts, performance dashboards, and automated reporting. Includes anomaly detection and automated response mechanisms for critical events. |
Clustering Portfolio Analytics
Cluster return-volatility map and validation backtest vs S&P 500Adaptive Portfolios Analytics
OLPS equity curves and performance metrics across allocation algorithms| 1. | Diversified Stock Portfolio Using Clustering Analysis S&P 500 portfolio construction using K-means clustering on risk/return features (correlation, beta, returns, volatility, Sharpe ratio). Backtested vs index. | Active | 2024 | Python, K-means, backtesting |
| 2. | Relative Rotation Graph (RRG) — US Equity Dynamic RRG for US stocks vs S&P 500. JdK RS-Ratio and RS-Momentum with animation. | Active | 2025 | Python, yfinance, Recharts |
| 3. | Adaptive Portfolio Strategies: Sequential Allocation Methods Comprehensive analysis of 14 sequential portfolio allocation strategies on diversified ETF portfolio. Includes momentum-based, reversion-based, and pattern-learning approaches with transaction cost analysis. | Active | 2025 | Python, sequential optimization, backtesting |
| 4. | Optimal Execution with RL Agent (DQN) Deep Q-Learning execution agent for slicing large orders under microstructure-style market impact. Compared against TWAP, passive, aggressive, and random baselines. | Active | 2026 | Python, Gymnasium, Stable-Baselines3, execution simulation |
| 5. | Market Regime Detection Using Gaussian Models Comprehensive market regime identification across 21 global indices using Gaussian Mixture Models (GMM) and Greedy Gaussian Segmentation (GSS). Detects bull, bear, and transition regimes for adaptive portfolio management. | Active | 2025 | Python, GMM, GSS, regime detection |
| 6. | Stock Sentiment Tracker US equity sentiment tracker using VADER lexical analysis on financial news headlines. Tracks prices, sentiment scores, and correlations for S&P 500 stocks. Updated daily. | Active | 2025 | Python, VADER, NLP, financial news APIs |
| 7. | Yield Curve Intelligence Yield-curve research project built from yfinance market data: Nelson-Siegel fitting, slope/inversion diagnostics, and regime-aware risk context. | Active | 2026 | Python, yfinance, Nelson-Siegel, macro risk signals |
| 1. | Adaptive Portfolio Optimization Under Non-Stationary Market Conditions | Journal of Quantitative Finance | 2024 | Novel approach to dynamic asset allocation using regime-switching models and reinforcement learning. |
| 2. | High-Frequency Data Processing: Computational Frameworks | Computational Economics | 2024 | Scalable architectures for real-time processing of tick-level financial data. |
| 3. | Robust Risk Measures in Fat-Tailed Distributions | Risk Analysis | 2023 | Comparative study of tail risk metrics under various distributional assumptions. |
| 1. | Statistical Modeling | Advanced regression analysis, time series forecasting, and multivariate statistical methods for complex data patterns. |
| 2. | Strategy Comparator | Compare up to 4 strategies side-by-side using Sharpe, return, drawdown, win rate, and trade count. |
| 3. | Stock Sentiment Tracker | US equity only: live prices and news sentiment (VADER) for major US stocks. See Sentiment page. |
| 4. | Risk Analytics | Comprehensive risk assessment frameworks including VaR, CVaR, and stress testing methodologies. |
| 5. | Data Pipeline | High-performance data processing infrastructure designed for large-scale quantitative analysis. |
| 6. | HFT Latency Budget Calculator | Plan per-stage p99.9 tick-to-trade budgets and detect bottlenecks before live deployment. |
| 7. | Portfolio Optimization | Modern portfolio theory implementation with multi-objective optimization and constraint handling. |