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.
2026·J/K/N · Deflated Sharpe · CSCV/PBO · walk-forward · stress tests
Multi-stage overfitting diagnostics for S&P 500 J/K/N winners-only momentum: DSR, disparity, sensitivity, block bootstrap, CSCV/PBO, walk-forward CV, and stress-tested selection.
2026·US equity · US bonds · REITs · Sharpe tests · drawdown tests · interactive diagnostics
Comparative allocation study of US equity-only versus US multi-asset portfolios (stocks, bonds, REITs) with hypothesis testing, frontier diagnostics, and interactive risk-adjusted analytics.
2026·Python, Recharts, PCA, shrinkage, cross-sectional R²
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·Crisis replay · compound stress · VaR/ES suite · FFP reweighting · regime correlation
Institutional portfolio stress laboratory: historical crisis replay, univariate and compound parametric shocks, historical/parametric/Monte Carlo tail risk, Fully Flexible Probability reweighting, forward GARCH simulation, and cross-study tail-risk attribution.
2026·MST · TMFG · Centrality · Neighborhood MIP
MST and TMFG networks on Nifty 50 with average-centrality and neighborhood constraints inside mean–variance programs, compared with HRP, HERC, and NCO.
2026·FF6 · Nifty indices · Fund attribution
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.
2026·12-1 momentum · 144 configurations · net of costs
A systematic grid study of long-only 12-1 momentum on Indian equities: how rebalance interval, universe breadth, holdings count, and weighting scheme interact — with overlapping portfolios, transaction costs, and six-factor attribution.
2026·Sector baskets · Industry cycle · 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.
Data as of 2026-07-18
Abbreviations
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.
This 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
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.
Composite leaders and average value, quality, and growth scores by sector from the latest US large-cap fundamentals dataset.
View fundamental stock analysis projectGenerated Saturday, Jul 18, 2026
| # | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Chaikin Oscillator | Chaikin Oscillator | 0.98 | 56% | +65.8% | -7.2% | +51.4% | 37 | 3% | 5% |
| 2 | WMA Crossover | WMA Crossover | 0.94 | 67% | +34.1% | -8.3% | +60.0% | 20 | 3% | 5% |
| 3 | Detrended Price Oscillator | Dpo | 0.88 | 56% | +51.2% | -8.1% | +46.1% | 26 | 0% | 5% |
| 4 | Bollinger Bands | Bollinger Bands | 0.87 | 56% | +76.0% | -12.8% | +80.0% | 15 | 7% | 10% |
| 5 | Twiggs Money Flow | Twiggs Money Flow | 0.87 | 63% | +51.7% | -10.3% | +50.0% | 22 | 5% | 5% |
Showing 1 to 5 of 71 strategies
Ranked snapshot of strategy performance metrics.
No fundamental strategy dataset found yet. Run npm run data:fundamental-strategies.
Income
Income
Income
Neutral
Neutral
Neutral
Directional
Directional
| # | |||||||
|---|---|---|---|---|---|---|---|
| 1 | Cash-Secured Put | Income | 4.65 | +28.8% | +1.4% | +96.1% | 4 |
| 2 | Short Puts | Income | 4.65 | +28.8% | +1.4% | +96.1% | 4 |
| 3 | Cash-Secured Put | Income | 4.65 | +28.8% | +1.4% | +96.1% | 4 |
| 4 | Short Strangle | Neutral | 1.44 | +47.0% | +2.0% | +85.5% | 4 |
| 5 | Short Strangles | Neutral | 1.44 | +47.0% | +2.0% | +85.5% | 4 |
| 6 | Jade Lizard | Neutral | 0.97 | +30.8% | +2.7% | +75.0% | 4 |
| 7 | 1x2 Call Ratio Spread | Directional | 0.92 | +709.5% | +33.7% | +77.6% | 4 |
| 8 | Call Front Spread | Directional | 0.92 | +709.5% | +33.7% | +77.6% | 4 |
Loading portfolio strategies…
Per-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.
| Cluster | Count | Return | Vol | Sharpe | Beta |
|---|---|---|---|---|---|
| 1 | 56 | 24.9% | 33.1% | 0.78 | 1.09 |
| 2 | 12 | -8.4% | 59.0% | -0.15 | 1.94 |
| 3 | 41 | -9.1% | 32.4% | -0.29 | 0.87 |
| 4 | 86 | 6.3% | 24.6% | 0.27 | 0.71 |
Mean return
Mean volatility
Mean Sharpe
Online 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.
| Algorithm | Type | Sharpe ↓ | Return | Vol | Max DD |
|---|---|---|---|---|---|
| PAMR | Follow-the-Loser | 2.88 | 104.90% | 15.10% | -9.20% |
| OLMAR | Follow-the-Loser | 2.07 | 70.21% | 14.81% | -6.67% |
| BCRP | Benchmark | 1.94 | 57.24% | 13.21% | -10.72% |
| RMR | Follow-the-Loser | 1.18 | 38.12% | 14.92% | -7.99% |
| Anticor | Follow-the-Loser | 1.12 | 24.36% | 10.37% | -9.15% |
| BAH | Benchmark | 0.86 | 18.12% | 10.10% | -11.57% |
| EG | Follow-the-Winner | 0.85 | 17.80% | 10.06% | -11.58% |
| CRP | Benchmark | 0.85 | 17.78% | 10.05% | -11.58% |
| ONS | Follow-the-Winner | 0.85 | 17.77% | 10.05% | -11.58% |
| CWMR | Follow-the-Loser | 0.85 | 17.78% | 10.05% | -11.58% |
| CORN | Pattern Matching | 0.85 | 17.77% | 10.05% | -11.58% |
| BNN | Pattern Matching | 0.85 | 17.76% | 10.05% | -11.58% |
| UP | Follow-the-Winner | 0.82 | 17.90% | 10.48% | -12.14% |
| Kelly | Pattern Matching | -0.03 | -0.67% | 11.14% | -18.57% |
| 1. | Nifty 50 Graph-Constrained Portfolio Optimization India · MST and TMFG networks on Nifty 50 with average-centrality and neighborhood constraints inside mean–variance programs, compared with HRP, HERC, and NCO. | 2026 | MST · TMFG · Centrality · Neighborhood MIP |
| 2. | Detecting Overfitting in Momentum Strategies Global · Multi-stage overfitting diagnostics for S&P 500 J/K/N winners-only momentum: DSR, disparity, sensitivity, block bootstrap, CSCV/PBO, walk-forward CV, and stress-tested selection. | 2026 | J/K/N · Deflated Sharpe · CSCV/PBO · walk-forward · stress tests |
| 3. |  US Equity vs US Multi-Asset Risk-Adjusted Study Global · Comparative allocation study of US equity-only versus US multi-asset portfolios (stocks, bonds, REITs) with hypothesis testing, frontier diagnostics, and interactive risk-adjusted analytics. | 2026 | US equity · US bonds · REITs · Sharpe tests · drawdown tests · interactive diagnostics |
| 4. |  India Six-Factor Premia, Attribution & Regime Analysis India · 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 |
| 5. |  Momentum Cadence and Portfolio Design in Indian Equities India · A systematic grid study of long-only 12-1 momentum on Indian equities: how rebalance interval, universe breadth, holdings count, and weighting scheme interact — with overlapping portfolios, transaction costs, and six-factor attribution. | 2026 | 12-1 momentum · 144 configurations · net of costs |
| 6. |  NIFTY 50 Seasonal Analysis by Industry & SARIMA India · 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 |
| 7. |  Nifty 50 Value-Momentum-Size Long-Short Strategy India · 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 |
| 8. |  Nifty 50 Alpha101 Selection & Composite Factor Research India · 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 |
Showing 1–8 of 40 projects
Updated about 10 hours ago (latest item)
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| 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. |