Type: Chaikin OscillatorSharpe: 1.01Return: +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.
What's Here
Quantitative strategies, options analysis, portfolio optimization, and market researchmoreStrategy 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.
| 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 strategies (ranked by Sharpe)Data as of 2026-05-19
Top Equity Strategies
ShRetMDDWin
Top Options Strategies
ShRetMDDWin
Top Portfolio Models
ShRetMDDVolSortCalm
Abbreviations
- Sh
- Sharpe ratio (risk-adjusted return)
- Ret
- Average total return
- MDD
- Maximum drawdown
- Win
- Win rate
- Vol
- Annualized volatility (portfolios)
- Sort
- Sortino ratio (portfolios)
- Calm
- Calmar ratio (portfolios)
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.
10Y-3M Spread
1.055
Latest Regime
Steep Growth
Inversion Frequency
22.801%
Fundamental Stock Analysis
Dropdown-driven US large-cap fundamentals snapshotSelect a metric to compare the available US universe using the latest generated dataset.
View MoreUpdated: 2026-05-19 · 495 symbols with fundamentals
Risk vs Return (1Y)
Value vs Quality factor map
Universe distribution · Trailing P/E
469 symbols · 469 binsSector mean comparison (Trailing P/E)
Basic MaterialsCommunication ServicesConsumer CyclicalConsumer DefensiveEnergyFinancial ServicesHealthcareIndustrialsReal EstateTechnologyUtilities
Risk reports & stop / take-profit analysis
moreHow diversified equity and options backtests look in risk–return space, plus a controlled experiment on stops and profit targets. 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
Each scatter dot is one strategy averaged across all index backtests (left). On the right, pick any strategy from the dropdown to see that same cross-index mean as an equity and drawdown path.
Scatter plot
Equity strategies: Sharpe vs maximum drawdownEach dot is one strategy, with Sharpe and max drawdown averaged across all 20 index backtests. Compare rules on a level playing field, not a single lucky market.
Side-by-side panels
All strategies: cross-index average pathsUse the dropdown to pick any strategy. Each path averages normalized equity and drawdown across every index backtest (same basis as the scatter).
Options on US index ETFs
54 options strategies on SPY, QQQ, IWM, and DIA (metrics averaged per strategy). Stacked vertical bar charts show win rate and avg return per trade separately (see chart caption). Bar and scatter colors use strategy category (Income, Directional, Spread, Volatility, Neutral, Hedge). All four charts use the same simulated exits on the underlying ETF (stop, target, or 45-DTE expiry).
Scatter (color = total return)
Options strategies: Sharpe vs drawdown (avg across ETFs)One dot per strategy: mean Sharpe and drawdown across SPY, QQQ, IWM, and DIA. Metrics come from trades that can exit on underlying stop-loss / take-profit or at 45-DTE expiry (not hold-to-expiry only when risk controls are on).
Reading the chart
- Sample period: Aug 2024 – Feb 2026 (~18 months), 19 rolling 45-DTE cycles on SPY, QQQ, IWM, and DIA.
- Same axes as the equity scatter: drawdown (%) vs Sharpe, but each point averages four ETF backtests.
- Each dot’s Sharpe and drawdown reflect realized P&L after simulated SL/TP on the ETF path (see risk note on the chart when present).
Vertical bar charts (two metrics)
Options strategies: return % vs win rate % (avg across ETFs)Vertical bar charts for all 54 strategies (metrics averaged across SPY, QQQ, IWM, and DIA). Win rate and avg return per trade use the same risk-managed exits as the scatter (underlying SL/TP when enabled, else 45-DTE expiry).
Reading the chart
- Sample period: Aug 2024 – Feb 2026 (~18 months) with 19 rolling 45-DTE entries, rolled every ~21 trading days on SPY, QQQ, IWM, and DIA (see report JSON for exact dates).
- Two stacked charts (not one shared axis): top = win rate (%), bottom = average return per trade (%). Summing every trade’s PnL % can exceed 1,000% and is not plotted; tooltip shows that sum on request.
- Bars are not “expiry-only” returns when risk controls are active — early stops and targets change both win rate and average return per trade.
Bar chart (counts)
How option trades exitedCounts trades by simulated exit rule: stop-loss and take-profit on the underlying ETF path, max-hold time, or hold to 45-DTE expiry. Requires options analysis run with risk controls enabled.
Reading the chart
- A single bar means every trade in the stored backtest exited at expiry — the JSON was built without underlying stop-loss / take-profit (re-run with --stop-loss-pct and --take-profit-pct).
- Multiple bars: compare how often stops cut losers vs targets bank gains vs holding through expiry.
- These are model exits on the index price path, not broker fill types (roll, assignment, etc.).
Scatter (color = trade P&L)
Per trade: MAE vs MFEMAE = worst unrealized loss versus entry while the trade was open. MFE = best unrealized profit before exit. Each point is one completed trade, exited via the same underlying SL/TP / expiry rules as the other options charts.
Reading the chart
- Horizontal: MAE (%), vertical: MFE (%). Points toward the upper-right had room to move both ways intratrade.
- Color shows realized P&L (%) at the simulated exit bar (stop, target, or 45-DTE expiry) — not P&L as if every trade ran full term.
- 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.
Composite bars
Stop / take-profit scenarios (static summary)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.
Line chart
Stop-loss only: average Sharpe vs stop widthOnly 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.
Line chart
Take-profit only: average Sharpe vs targetOnly 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.
Bar chart
Stop-loss only: average maximum drawdownFor 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.
Bar chart
Take-profit only: average maximum drawdownSame 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: 19 May 2026, 11:17.
Grouped bar chart
Compare scenarios: average Sharpe, return, and drawdown
Each cluster is one risk setting (baseline, stop only, target only, or both). Hover to see the full plain-English name. Short tick labels keep the layout compact.
- 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.
Line chart
Stop-loss only: how average Sharpe moves
Take-profit is held fixed (off). Only the stop under the entry price changes. The first point is the baseline with no mechanical stop in this experiment.
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).
Line chart
Take-profit only: how average Sharpe moves
Stop-loss is held fixed (off). Only the profit target above entry changes. The first point is the baseline with no mechanical take-profit in this experiment.
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.
Bar chart
Stop-loss only: average depth of drawdown
Each bar is the average, across strategies, of that strategy's worst peak-to-trough loss (%) for the matching stop setting.
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.
Bar chart
Take-profit only: average depth of drawdown
Same averaging rule as the stop-only drawdown chart, but only profit targets change. The first category is the baseline for this sweep.
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 Tuesday, May 19, 2026
Type:
Type: WMA CrossoverSharpe: 0.95Return: +34.1%Max DD: -8.3%Win: +60.0%Trades: 20SL/TP: 3% / 5%
Type: DpoSharpe: 0.91Return: +51.8%Max DD: -8.1%Win: +45.8%Trades: 24SL/TP: 0% / 5%
Type: Twiggs Money FlowSharpe: 0.88Return: +51.7%Max DD: -10.3%Win: +50.0%Trades: 22SL/TP: 5% / 5%
Type: Bollinger BandsSharpe: 0.87Return: +74.3%Max DD: -12.8%Win: +78.6%Trades: 14SL/TP: 7% / 10%
| # | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| 1 | Chaikin Oscillator | Chaikin Oscillator | 1.01 | +67.1% | -7.2% | +52.8% | 36 | 3% | 5% |
| 2 | WMA Crossover | WMA Crossover | 0.95 | +34.1% | -8.3% | +60.0% | 20 | 3% | 5% |
| 3 | Detrended Price Oscillator | Dpo | 0.91 | +51.8% | -8.1% | +45.8% | 24 | 0% | 5% |
| 4 | Twiggs Money Flow | Twiggs Money Flow | 0.88 | +51.7% | -10.3% | +50.0% | 22 | 5% | 5% |
| 5 | Bollinger Bands | Bollinger Bands | 0.87 | +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: 1.02Return: +2024.2%Max DD: +100.6%Win: +84.2%Tickers: 4
Volatility
Sharpe: 0.96Return: +2118.8%Max DD: +100.7%Win: +84.2%Tickers: 4
Volatility
Sharpe: 0.96Return: +2118.8%Max DD: +100.7%Win: +84.2%Tickers: 4
Volatility
Sharpe: 0.79Return: +1771.0%Max DD: +222.7%Win: +81.6%Tickers: 4
Volatility
Sharpe: 0.79Return: +649.6%Max DD: +74.9%Win: +81.6%Tickers: 4
Volatility
Sharpe: 0.56Return: +14644.2%Max DD: +584.6%Win: +46.1%Tickers: 4
Volatility
Sharpe: 0.55Return: +16258.1%Max DD: +587.4%Win: +46.1%Tickers: 4
Volatility
Sharpe: 0.55Return: +16258.1%Max DD: +587.4%Win: +46.1%Tickers: 4
| # | |||||||
|---|---|---|---|---|---|---|---|
| 1 | Reverse Iron Butterfly | Volatility | 1.02 | +2024.2% | +100.6% | +84.2% | 4 |
| 2 | Long Straddle | Volatility | 0.96 | +2118.8% | +100.7% | +84.2% | 4 |
| 3 | Long Straddles | Volatility | 0.96 | +2118.8% | +100.7% | +84.2% | 4 |
| 4 | Short Put Calendar | Volatility | 0.79 | +1771.0% | +222.7% | +81.6% | 4 |
| 5 | Short Call Calendar | Volatility | 0.79 | +649.6% | +74.9% | +81.6% | 4 |
| 6 | Reverse Iron Condor | Volatility | 0.56 | +14644.2% | +584.6% | +46.1% | 4 |
| 7 | Long Strangle | Volatility | 0.55 | +16258.1% | +587.4% | +46.1% | 4 |
| 8 | Long Strangles | Volatility | 0.55 | +16258.1% | +587.4% | +46.1% | 4 |
Portfolio Optimization
30 systematic strategies using 495 S&P 500 stocks
Best Return
703.5%
Best Sharpe
4.79
Lowest Vol
9.5%
Avg Sharpe
3.17
Risk-Return Profile
What this shows: Each bubble is one strategy positioned by annualized volatility (x-axis) and annualized return (y-axis), with bubble size tied to Sharpe ratio.
How to read it: Higher and more leftward bubbles are generally better risk-adjusted candidates; compare bubble sizes to identify strategies with stronger return per unit volatility.
Aggressive
Defensive
Equal Weight
Low Volatility
Mean Reversion
Momentum
Multi-Factor
Quality
Risk Parity
Rotation
Trend
Volume
Top Strategies by Sharpe Ratio
Data as of Tuesday, May 19, 2026
| Rank | ||||||
|---|---|---|---|---|---|---|
| 1 | Quality Composite Score | Quality | 189.0% | 22.7% | 4.79 | -9.2% |
| 2 | All Weather Portfolio | Defensive | 152.5% | 20.4% | 4.66 | -7.8% |
| 3 | 12-Month Momentum Top 10 | Momentum | 401.0% | 39.2% | 4.32 | -14.6% |
| 4 | High Sharpe Ratio Top 10 | Quality | 154.2% | 23.6% | 4.08 | -7.9% |
| 5 | Momentum + Quality | Multi-Factor | 241.8% | 31.6% | 4.05 | -11.8% |
Showing 1-5 of 30 strategies
Page 1 of 6
| 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. |
View:
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Clustering Portfolio Analytics
moreS&P 500 names are grouped with K-means on scaled risk/return features—annual return, volatility, Sharpe ratio, beta, correlation, and related inputs—so each cluster captures a distinct cross-sectional risk profile. Portfolios are built by equal-weighting the highest-Sharpe names from every cluster, with a separate top-20 Sharpe basket and the S&P 500 index as benchmarks.
Features are estimated on the training window and cluster assignments are frozen before validation. The elbow method on within-cluster sum of squares guides the choice of K; the live run uses K = 4 clusters across roughly 200 liquid constituents, then scores out-of-sample cumulative wealth on the held-out validation dates below.
The panels summarize universe setup, per-cluster training statistics, a return–volatility map (hover any point for the ticker), mean Sharpe by cluster, and validation paths for the three portfolio definitions. Full symbol lists, feature-correlation detail, and methodology 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 summarize universe setup, leading test-period strategies, equity paths for the top in-sample Sharpe selectors, a cross-sectional risk–return map, average Sharpe by algorithm family, and the full metrics table. For methodology, fee stress tests, and return distributions, see the Adaptive Portfolio Strategies project page.
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| 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. | Fundamental Stock Analysis US large-cap fundamental analytics dashboard with deep cross-sectional ranking, distribution diagnostics, sector structure, risk/return mapping, and multi-factor composite interpretation. | Active | 2026 | Python, fundamental screening, interactive charts |
| 6. | 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 |
| 7. | 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 |
| 8. | 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 |
| 9. | Fama–French Factor Lab Unified factor dashboard: US FF5 and Carhart momentum, multi-asset ETF panel, international sleeve comparison, volatility regimes, risk budgeting, sleeve attribution, SPY research grid, and q-factor extension notes. Refreshed from Ken French + yfinance. | Active | 2026 | Python, pandas, linear factor models, Ken French data, yfinance |
| 10. | Smart Beta: Factor-Tilted Portfolio Construction Sector-balanced S&P 500 panel: equal-weight, min-variance, value (HML), and value–momentum sleeves with Ken French factors, 250-day loadings, daily rebalance (2017 window). | Active | 2026 | Python, scipy, Ken French factors, yfinance |
| 11. | 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. | Active | 2026 | Python, Recharts, PCA, shrinkage, cross-sectional R² |
| 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. |
