Learn quant by following live strategies.
Every signal explains itself.
Sixteen academically-grounded strategies running on real markets — trend, mean-reversion, carry, breakout, ML, regime switching, event-driven, and portfolio overlays — with the math, parameters, and historical context behind every signal. Built for traders who want to understand, not just follow.
Free to start. Upgrade anytime.
Market data via OANDA, Yahoo Finance, and CoinGecko
Try it yourself
Drag the slider, feel what parameters do
TSMOM's 'lookback window' determines how far back the strategy looks. Below is the same strategy, same assets, same period — only the lookback changes.
Lookback window
252 days (≈12mo)paper default
3-year return
+7.6%
Sharpe
0.35
Max DD
-10.8%
Trades
21
12 months — the paper's choice. Moskowitz, Ooi, Pedersen (2012) settled on this after extensive testing across their 1985-2009 sample. It's robust across most regimes.
7 assets · real data, past 3 years
Live Performance
3-year backtested equity curves on real markets
These are real curves — nothing mocked, nothing cherry-picked. Each strategy's parameters are grid-searched, then the optimized strategy is run on 3 years of real history. Every daily equity point is downloadable as CSV for you to verify.
Past performance does not indicate future results. Backtests use OANDA forex data with 1-pip spread cost per trade.
Why OpenAlpha
Research-grade, inside out
Complete Transparency
Every signal is accompanied by its exact mathematical parameters. Z-scores, p-values, hedge ratios, feature importance — all visible. No black boxes.
Academic Foundations
Every strategy is a published academic paper made executable. We cite the original work, link the code, and show you the assumptions — not just the result.
Rigorous Validation
Walk-forward cross-validation. Out-of-sample testing. Purged k-fold to prevent data leakage. Every backtest is scientifically defensible.
Open & affordable
Free forever for the strategy library and source code. $29/mo for real-time signals and the educational walkthroughs. No credit card to start.
Who this is for
Built for people who want to understand, not just follow
OpenAlpha fits you if you...
Data scientists and engineers who want to apply their skills to markets
Finance students and CFA candidates studying quantitative methods
Self-directed traders curious about how systematic strategies actually work
Anyone tired of black-box signal services who wants to verify the math
OpenAlpha is NOT for you if you...
Day traders looking for 1-minute scalping signals
PhD quants — you'll be writing your own from the same papers
People who just want to be told what to buy without understanding why
We're explicit about who OpenAlpha isn't for. We'd rather build the best experience for the right audience than make vague promises to attract everyone.
How you learn
Read the math, watch the strategy, run your own experiments
Read the textbook
Each strategy comes with the original academic paper, the math, the parameters, and when it works (and fails).
Watch it run live
Today's signals are live examples — not just 'buy this' but 'here's why this pattern emerged in the current market'.
Run your own
Backtest with custom parameters in the lab, save your configurations, and see how your variants would have performed.
Strategy Library
Sixteen quantitative strategies grounded in academic research
Pair Trading
Identify temporary price divergences between statistically related assets
Based on Gatev, Goetzmann & Rouwenhorst (2006), 'Pairs Trading: Performance of a Relative-Value Arbitrage Rule', Review of Financial Studies
TSMOM
Time-series momentum: measure trend persistence across multiple assets
Based on Moskowitz, Ooi & Pedersen (2012), 'Time Series Momentum', Journal of Financial Economics
Cross-Asset Pairs
Trade asset pairs connected by real economic relationships
Based on Gatev, Goetzmann & Rouwenhorst (2006), 'Pairs Trading: Performance of a Relative-Value Arbitrage Rule', Review of Financial Studies
Cointegration Basket
Find stationary combinations across multiple assets simultaneously
Based on Johansen (1988), 'Statistical Analysis of Cointegration Vectors', Journal of Economic Dynamics and Control
XGBoost ML
Machine learning predicts price direction using dozens of market features
Walk-forward methodology based on Lopez de Prado (2018), 'Advances in Financial Machine Learning'
HMM Regime
Automatically detect market regimes and adjust strategy allocation
Based on Hamilton (1989), 'A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle', Econometrica
How we compare
Why pick OpenAlpha over the alternatives
We're not reinventing the wheel — we're filling the gaps the others leave: black boxes, steep learning curves, missing pedagogy.
| OpenAlpha | TradingView | QuantConnect | Signal groups | |
|---|---|---|---|---|
| Strategy source code | Open source (MIT) | Closed Pine Script | Open algos, closed engine | Black box |
| Academic paper citations | Yes — every strategy | Rare | Sometimes | Almost never |
| Why this signal? explanation | Built into every signal | Charts only | Code-first | "Trust me bro" |
| Real-time backtest | Yes (in browser) | Yes | Yes (cloud IDE) | No |
| Setup time before first run | 0 minutes | 5 minutes | Hours (API, IDE, deps) | Pay first |
| Pricing entry point | $0 free / $29 paid | $0 / $14 / $30 / $60 | $0 / $20 / $50+ | $30-300/month |
| Verifiable track record | Yes — download CSV | User-published only | Yes (your own) | Screenshots |
Comparison based on publicly available information as of April 2026. OpenAlpha is not affiliated with any product listed.
Pricing
Pick the plan that fits you
Start free, upgrade anytime. No credit card required to begin.
Free
- All 16 strategies (delayed signals)
- Full strategy documentation
- 1-year backtest history
- GitHub source code
Learner
- Real-time signals
- Signal explanations & context
- 5-year backtest history
- Custom parameters
- Weekly market reports
Pro
- Everything in Learner
- 10-year backtest history
- Parameter optimization
- API access
- Webhook alerts
Powered by peer-reviewed research
Ready to start learning?
Free forever for the strategy library and delayed signals. Upgrade to Learner ($29/mo) when you want to follow them in real time.
See Live SignalsOpen source on GitHub · No credit card required