Type: Chaikin OscillatorSharpe: 1.00Stat: 63%Return: +67.1%Max DD: -7.2%Win: +52.8%Trades: 36SL/TP: 3% / 5%
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. We backtest 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. Each strategy is documented with methodology, assumptions, risk metrics, drawdown behavior, and implementation notes so readers can evaluate both strengths and limitations before using any idea in practice. We focus on reproducible workflows: clear inputs, explainable rules, out-of-sample checks, and comparable reporting across assets and market regimes. From indicator research and options structures to portfolio construction and risk-control experiments, the goal is simple: make serious quantitative research transparent, verifiable, and useful for independent builders.
What's Here
Quantitative strategies, options analysis, portfolio optimization, and market research moreStrategy Library
70+ technical indicator-based strategies backtested across 21 global equity indices with 5 years of daily data. Each strategy page includes equity and drawdown paths, trade diagnostics, risk-adjusted metrics, parameter sensitivity context, and Python reference code so ideas can be reviewed and reproduced instead of treated as black-box signals.
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. Results are broken down per symbol with return, drawdown, win-rate, exit behavior, and implementation notes so premium, directional, spread, and hedge structures can be compared on the same basis.
Portfolio Optimization
30 systematic portfolio strategies built on S&P 500 stocks using factor signals (momentum, volatility, quality, trend). Skfolio-powered optimization compares Max Sharpe, Min Variance, Risk Parity, HRP, and related allocators across US and global universes with transparent assumptions, constraints, and risk-return tradeoff reporting.
Parameter Optimization
Grid search over each strategy's key parameters, optimizing for Sharpe ratio, Sortino, Calmar, win rate, and profit factor. Outputs are shown as line charts (1-parameter) and scatter/heatmap surfaces (2-parameter) so you can identify robust ranges, instability zones, and overfit-looking peaks before selecting settings.
Market Analysis
Relative Rotation Graphs for US equity and sector ETFs vs S&P 500, correlation matrix analysis across strategies, stock sentiment tracking with VADER on financial headlines, and market regime detection via GMM/HMM models. These tools provide cross-sectional and regime context to interpret when a strategy environment is supportive, mixed, or hostile.
Project Research
Project research modules cover adaptive portfolios (OLPS), K-means clustering portfolio construction, execution/risk experiments, and regime-aware studies. Each project pairs interactive visualizations with methodology documentation, assumptions, and source-linked implementation details to keep the research auditable and practical.
| Strategy Library | 70+ technical indicator-based strategies backtested across 21 global equity indices with 5 years of daily data. Each strategy page includes equity and drawdown paths, trade diagnostics, risk-adjusted metrics, parameter sensitivity context, and Python reference code so ideas can be reviewed and reproduced instead of treated as black-box signals. |
| 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. Results are broken down per symbol with return, drawdown, win-rate, exit behavior, and implementation notes so premium, directional, spread, and hedge structures can be compared on the same basis. |
| Portfolio Optimization | 30 systematic portfolio strategies built on S&P 500 stocks using factor signals (momentum, volatility, quality, trend). Skfolio-powered optimization compares Max Sharpe, Min Variance, Risk Parity, HRP, and related allocators across US and global universes with transparent assumptions, constraints, and risk-return tradeoff reporting. |
| Parameter Optimization | Grid search over each strategy's key parameters, optimizing for Sharpe ratio, Sortino, Calmar, win rate, and profit factor. Outputs are shown as line charts (1-parameter) and scatter/heatmap surfaces (2-parameter) so you can identify robust ranges, instability zones, and overfit-looking peaks before selecting settings. |
| Market Analysis | Relative Rotation Graphs for US equity and sector ETFs vs S&P 500, correlation matrix analysis across strategies, stock sentiment tracking with VADER on financial headlines, and market regime detection via GMM/HMM models. These tools provide cross-sectional and regime context to interpret when a strategy environment is supportive, mixed, or hostile. |
| Project Research | Project research modules cover adaptive portfolios (OLPS), K-means clustering portfolio construction, execution/risk experiments, and regime-aware studies. Each project pairs interactive visualizations with methodology documentation, assumptions, and source-linked implementation details to keep the research auditable and practical. |
Top Performers
Equity leaders ranked by statistical validation score; options and portfolios by SharpeData as of 2026-06-08
Top Equity Strategies (by Stat score)
ShRetMDDWinStatTrdCov
Top Options Strategies
ShRetMDDWinTrdCov
Top Portfolio Models
ShRetMDDVolWinSortCalmVaRCVaRAvgDD
1.Max Sharpe Ratio
US portfolio
3.12+76.7%9.5%12.4%61.9%4.350.020.9%1.7%0.8%
2.Min Variance
US portfolio
1.91+39.1%8.2%10.3%55.6%2.630.010.8%1.4%1.1%
3.Min CVaR
US portfolio
1.89+40.3%8.2%10.8%55.2%2.680.010.8%1.3%1.2%
4.Max Diversification
US portfolio
1.88+42.6%9.8%11.4%57.1%2.610.011.0%1.5%1.1%
5.Min Semi-Variance
US portfolio
1.85+38.4%8.7%10.5%55.2%2.620.010.8%1.3%1.1%
Abbreviations
- Sh
- Sharpe ratio (risk-adjusted return)
- Ret
- Average return
- MDD
- Maximum drawdown
- Win
- Win rate
- Stat
- Cumulative statistical efficiency score (% validation gates passed)
- Trd
- Average trades per symbol
- Cov
- Number of symbols covered
- Vol
- Annualized volatility
- Sort
- Sortino ratio (downside risk-adjusted return)
- Calm
- Calmar ratio (return / max drawdown)
- VaR
- 95% daily value-at-risk
- CVaR
- 95% daily conditional value-at-risk
- AvgDD
- Average drawdown
Yield Curve Intelligence
This panel summarises US Treasury term-structure dynamics: inversion diagnostics on benchmark spreads, Nelson–Siegel level, slope, and curvature, and a macro risk regime that combines curve shape with equity drawdown context. It is a condensed view of the live monitor in our Yield Curve Intelligence study, where the full factor history, regime tables, and methodology are documented.
The figures below update from the latest available curve snapshot. The headline cards report the current 10Y–3M spread, the classified regime, and how often the curve has been inverted over the sample; the chart traces the same spreads through the most recent sessions so you can judge whether flattening is isolated or persistent.
Read spreads against zero: negative values mean short rates exceed long rates for that pair. A brief dip below zero is not, by itself, a recession signal; sustained inversion—especially when it aligns with risk-off equity conditions—has more often preceded macro slowdowns, though lead times and severity still vary by cycle.
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US Sector Seasonality
moreThis section tracks recurring calendar effects in US equity sectors using eleven SPDR Select Sector ETFs (GICS) and SPY as the benchmark. Each heatmap cell is the unconditional average monthly total return for that sector in that calendar month, pooled across all years in the sample from 2019 onward.
What this widget shows
- Heatmap — sector rows × calendar-month columns; green cells mark historically positive months, red cells mark negative drift.
- Seasonality dispersion (σ) — how much each group's calendar profile varies month-to-month; cyclicals typically show wider swings than defensives.
- SPY best month — the calendar month with the highest average broad-market return in the sample window.
Full methodology, cyclical vs defensive tables, quarterly profiles, benchmark-relative rotation, and exploratory t-tests are on the Calendar Effects in US GICS Sector ETFs project page.
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Fundamental Stock Analysis
Composite leaders and sector-level factor scoresComposite leaders and average value, quality, and growth scores by sector from the latest US large-cap fundamentals dataset.
View MoreLoading fundamental data…
Generated Monday, Jun 8, 2026
Type:
Type: WMA CrossoverSharpe: 0.95Stat: 63%Return: +34.1%Max DD: -8.3%Win: +60.0%Trades: 20SL/TP: 3% / 5%
Type: DpoSharpe: 0.91Stat: 63%Return: +51.8%Max DD: -8.1%Win: +45.8%Trades: 24SL/TP: 0% / 5%
Type: Stochastic RsiSharpe: 0.88Stat: 50%Return: +73.5%Max DD: -9.3%Win: +70.0%Trades: 40SL/TP: 5% / 10%
Type: Bollinger BandsSharpe: 0.87Stat: 50%Return: +74.3%Max DD: -12.8%Win: +78.6%Trades: 14SL/TP: 7% / 10%
| # | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Chaikin Oscillator | Chaikin Oscillator | 1.00 | 63% | +67.1% | -7.2% | +52.8% | 36 | 3% | 5% |
| 2 | WMA Crossover | WMA Crossover | 0.95 | 63% | +34.1% | -8.3% | +60.0% | 20 | 3% | 5% |
| 3 | Detrended Price Oscillator | Dpo | 0.91 | 63% | +51.8% | -8.1% | +45.8% | 24 | 0% | 5% |
| 4 | Stochastic RSI | Stochastic Rsi | 0.88 | 50% | +73.5% | -9.3% | +70.0% | 40 | 5% | 10% |
| 5 | Bollinger Bands | Bollinger Bands | 0.87 | 50% | +74.3% | -12.8% | +78.6% | 14 | 7% | 10% |
Showing 1 to 5 of 71 strategies
Page 1 of 15
Ranked snapshot of strategy performance metrics.
No fundamental strategy dataset found yet. Run npm run data:fundamental-strategies.
Category:
Volatility
Sharpe: 1.00Return: +1806.0%Max DD: +189.7%Win: +82.9%Tickers: 4
Volatility
Sharpe: 0.99Return: +685.0%Max DD: +61.2%Win: +82.9%Tickers: 4
Volatility
Sharpe: 0.98Return: +2008.6%Max DD: +90.6%Win: +82.9%Tickers: 4
Volatility
Sharpe: 0.97Return: +2036.1%Max DD: +90.9%Win: +82.9%Tickers: 4
Volatility
Sharpe: 0.97Return: +2036.1%Max DD: +90.9%Win: +82.9%Tickers: 4
Volatility
Sharpe: 0.56Return: +17603.5%Max DD: +498.1%Win: +42.1%Tickers: 4
Volatility
Sharpe: 0.56Return: +17998.8%Max DD: +498.2%Win: +42.1%Tickers: 4
Volatility
Sharpe: 0.56Return: +17998.8%Max DD: +498.2%Win: +42.1%Tickers: 4
| # | |||||||
|---|---|---|---|---|---|---|---|
| 1 | Short Put Calendar | Volatility | 1.00 | +1806.0% | +189.7% | +82.9% | 4 |
| 2 | Short Call Calendar | Volatility | 0.99 | +685.0% | +61.2% | +82.9% | 4 |
| 3 | Reverse Iron Butterfly | Volatility | 0.98 | +2008.6% | +90.6% | +82.9% | 4 |
| 4 | Long Straddle | Volatility | 0.97 | +2036.1% | +90.9% | +82.9% | 4 |
| 5 | Long Straddles | Volatility | 0.97 | +2036.1% | +90.9% | +82.9% | 4 |
| 6 | Reverse Iron Condor | Volatility | 0.56 | +17603.5% | +498.1% | +42.1% | 4 |
| 7 | Long Strangle | Volatility | 0.56 | +17998.8% | +498.2% | +42.1% | 4 |
| 8 | Long Strangles | Volatility | 0.56 | +17998.8% | +498.2% | +42.1% | 4 |
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| 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. |
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Clustering Portfolio Analytics
morePer-cluster training statistics—count, mean return, volatility, Sharpe, and beta—for K-means groups on scaled risk/return features. Full methodology, charts, and validation backtests are on the Diversified Stock Portfolio Clustering project page.
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Adaptive Portfolios Analytics
moreOnline portfolio selection (OLPS) updates weights each period from past prices alone—no forward-looking labels—making it a natural test bed for adaptive allocation under regime change. This study benchmarks fourteen published and baseline rules (momentum, mean-reversion, and pattern-learning families) on a diversified six-ETF sleeve spanning US equity, international equity, emerging markets, Treasuries, inflation-linked bonds, and REITs (VTI, EFA, EEM, TLT, TIP, VNQ).
Each algorithm is estimated on 2015–2022 daily closes and evaluated out-of-sample on 2023–2024 with daily rebalancing, zero look-ahead, and wealth indices reset to 1.0 at the test start. SPY and an equal-weight universe portfolio (UFR) anchor absolute performance; a 0.1% per-trade fee variant is tracked in the full project for turnover-sensitive strategies.
The panels below highlight the leading test-period strategies and a cross-sectional risk–return map across fourteen OLPS rules on a six-ETF sleeve (VTI, EFA, EEM, TLT, TIP, VNQ). For universe setup, equity paths, fee stress tests, and the full metrics table, see the Adaptive Portfolio Strategies project page.
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| 1. |  Cross-section shrinkage lab (ETF panel) Yahoo Finance ETF panel: PCA spectrum, cross-sectional R² vs K, pseudo-OOS folds, ridge diagnostics. JSON from npm run data:shrinking-cross-section. | 2026 | Python, Recharts, PCA, shrinkage, cross-sectional R² |
| 2. |  Market Homogeneity from Correlation Networks Empirical US study of market-wide co-movement: monthly homogeneity index from correlation networks, regime analysis, SPY conditioning, and interactive network graphs. | 2026 | Correlation networks · homogeneity index · regime analysis · SPY overlay · network graphs |
| 3. |  Global Capital Flow Intelligence System Multi-asset framework inferring global capital allocation from Yahoo Finance ETFs: eight flow engines, composite scores, regime detection, correlation networks, and flow-based portfolio backtests. | 2026 | Capital flow inference · 8 analytical layers · regime detection · flow portfolios · hypothesis tests |
| 4. |  Global Multi-Asset Portfolio for the Next Decade Institutional-style strategic asset allocation across 50+ global ETFs: eight frameworks from 60/40 to risk parity and max Sharpe, with factor attribution, regime splits, Monte Carlo paths, and crisis stress replay. | 2026 | Global ETFs · 60/40 vs diversified · risk parity · hypothesis tests · Monte Carlo |
| 5. |  CAPM vs Fama–French vs Carhart Goncharov (2023) replication: CAPM, FF3, and Carhart four-factor OLS on 30 US stocks (2018–2021) with Ken French factors. | 2026 | CAPM · FF3 · Carhart · adjusted R² · RSE tests |
| 1. |  India Six-Factor Premia, Attribution & Regime Analysis Six-factor study of Indian equities: long-run premia, Nifty style-index regressions, momentum crash risk, mutual-fund attribution, and whether quality pays in downturns — with interactive charts. | 2026 | FF6 · Nifty indices · Fund attribution |
| 2. |  NIFTY 50 Seasonal Analysis by Industry & SARIMA Report on NIFTY 50 calendar seasonality by industry: equal-weight sector baskets, cyclical vs defensive cycle spreads, benchmark-relative correlation, and SARIMA index diagnostics with full mathematical framework. | 2026 | Sector baskets · Industry cycle · SARIMA |
| 3. |  Nifty 50 Value-Momentum-Size Long-Short Strategy Nifty 50 Value, Momentum, and Size: Fama–MacBeth premia, IC/IR, and a 20%/20% long–short backtest on NSE data via yfinance — with interactive performance charts. | 2026 | Nifty 50 · Fama–MacBeth · L/S |
| 4. |  Nifty 50 Alpha101 Selection & Composite Factor Research Formulaic alphas on Nifty 50: cross-sectional cleaning, IC screening, linear and machine-learning composites, and quintile backtests — full research report with interactive charts. | 2026 | Alpha101 · IC · L/S quintiles |
| 5. |  Nifty 50 Multi-Factor Risk Model & Sector-Neutral Optimization Research report: daily style and sector risk factors on Nifty 50, rolling WLS covariance, and monthly sector-neutral long-only optimization with turnover costs. | 2026 | Risk model · Optimization · Backtest |
| 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. |
