A²PT · Strategy Learning Hub
Strategies, unpacked.
156 strategies. Learn the idea through a story, see the mechanics, then test the payoff against real observations.
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- 085Mean reversion & reversal
Short-term reversal
Short-term reversal typically buys recent weekly or monthly losers and shorts winners. Research attributes part of the effect to liquidity provision and correction of short-lived price pressure. Turnover, bid/ask spreads, asynchronous closes and extreme events can consume gross returns. Its horizon and mechanism differ from medium-term momentum.
- 086Factors & multi-factor investing
Sin stocks
Sin-stock research studies industries such as tobacco, alcohol and gaming that some investors exclude under social norms. Hong and Kacperczyk propose that lower institutional ownership may affect valuation and expected returns; this is descriptive market research, not a moral judgement. Regulation, litigation, taxes, ESG policies and sector risks can explain ownership and return differences.
- 087Mean reversion & reversal
Price–volume correlation alphas
This family ranks short-window correlations between price (or its rank) and volume, often taking the opposite side. Co-movement may reflect demand, information or liquidity shocks; correlation does not identify the cause. Formulaic Alpha #2 and related rules use different price transforms, volume transforms and windows.
- 088Mean reversion & reversal
VWAP deviation alphas
VWAP is the volume-weighted average transaction price for a chosen session and is commonly used as an execution benchmark. Mean-reversion formulas fade deviations, while breakout strategies may take the opposite side. Volume distribution, session, price benchmark and closing auctions affect the anchor; VWAP is not fundamental fair value.
- 089Trend & time-series momentum
Filter rule
Alexander’s filter rule buys after price rises x% from a recent low and sells or reverses after it falls x% from a recent high. Later studies vary the extreme window, filter size, holding period and market. Filters reduce small-move trades but delay entries and can whipsaw; historical rule tests need data-mining controls.
- 090Trend & time-series momentum
On-balance volume and volume momentum
OBV adds volume on up days and subtracts it on down days, forming a signed-volume series; rules then apply averages or breakouts to OBV. Volume momentum can also measure volume growth directly. Up/down close is only a proxy for direction and does not reveal aggressive order flow or market depth.
- 091Trend & time-series momentum
Momentum oscillator rules
ROC, directional indicators and moving-average deviations compress price changes into bounded or normalised values. Rules may follow a break above an overbought line or short a break below an oversold line, while other systems fade extremes. Current and prior readings are both needed to identify a crossover; markets and parameters require out-of-sample checks.
- 092Trend & time-series momentum
Directional movement
DMI smooths positive and negative directional movement into +DI and −DI; ADX measures trend strength, not direction. A common rule follows the larger DI when ADX is high and reduces trend exposure when ADX is low. Indicators require historical highs, lows and closes; ranges and parameter choice can cause whipsaws.
- 093Trend & time-series momentum
Chart patterns
Head-and-shoulders, triangles, rectangles, double tops/bottoms and broadening patterns can be algorithmically defined from highs and lows. Studies test conditional returns after pattern completion, but definitions, windows, smoothing and selection bias matter. This simulator does not detect charts; users enter fit, breakout direction and target/stop scenarios.
- 094Hedging & risk management
Risk parity
Risk parity allocates by risk contribution rather than capital share. Two-asset weights depend on volatility and correlation; leverage can then target portfolio volatility, so low-volatility bonds may be leveraged. Leverage, rising stress correlations, financing and rebalancing costs create risk.
- 095Hedging & risk management
Portfolio insurance：CPPI／OBPI
CPPI sets risky exposure to a multiplier times portfolio value above a floor, investing the remainder in cash or bonds. Rebalancing is needed as markets move; gaps can cause losses before exposure is cut and breach the floor. OBPI more directly constructs a floor using bonds and options.
- 096Options & payoff structures
Forecast-driven option strategy selection
This workflow first forms a directional or volatility outlook, then selects a limited-risk option structure: a bull spread for bullish views, a bear spread for bearish views, or an iron condor for a range view. Forecast error, implied volatility, expiry and liquidity matter; the signal model and option quotes need separate validation.