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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- 109Factors & multi-factor investing
Investor attention and media coverage
Sparse media coverage may raise information-acquisition frictions and coincide with higher expected returns. A sudden increase in attention proxies such as Google searches may create short-term retail buying pressure followed by reversal. The horizons differ, and attention measures, firm size and newsworthiness create biases.
- 110Factors & multi-factor investing
ESG signals
ESG rating disagreement can reflect differences in scope, indicator weights, sources and estimation methods rather than simply different views of sustainability performance. Research tests links with returns, risk and investor preferences, but results vary by sample, scales and sector. Separate average rating from disagreement and harmonise scales before comparison.
- 111Trend & time-series momentum
Factor momentum
Factor momentum uses each factor’s own recent return to set its holding direction: recent winners remain long and losers short. It differs from stock momentum; factor correlation, lookback, rebalancing and costs matter. Research finds factor time-series persistence can explain part of stock momentum, without guaranteeing future continuation.
- 112Factors & multi-factor investing
Prospect theory value
Prospect-theory value treats a historical return distribution as an investor’s mental representation, applying a gain/loss value function and probability weights to outcomes. Barberis, Mukherjee and Wang find lower subsequent average returns for high-value stocks. Parameters, historical windows and cumulative weighting affect ranks; this page uses a small discrete illustrative approximation.
- 113Global macro
Equity premium timing with predictive variables
Predictive regressions use dividend yields, term spreads, valuation, technical indicators and other inputs to forecast equity premia, then adjust equity allocation for risk. In-sample predictability often weakens out of sample; revisions, multiple testing, parameter instability and costs create gaps. This simulator maps a supplied premium into capped mean-variance exposure.
- 114Calendar & seasonality
Calendar anomalies
Turn-of-month, pre-holiday, weekday and January effects describe average calendar-time return differences. Early findings can reflect market structure, sample choices and multiple testing, and may decay after publication. This tool does not connect a trading calendar; it compares entered signal days, total days, period return and cash return.
- 115Machine learning
Evolved technical rules
Genetic algorithms or programming search trees of indicators, comparisons and logical operators, selecting, crossing and mutating rules by fitness. Larger search spaces intensify data snooping; reserve untouched time-ordered tests, include costs and record the search. Allen and Karjalainen find no robust US equity excess return after costs for their evolved rules.
- 116Market making & liquidity
Optimal execution
Almgren–Chriss splits a large order into time slices, balancing temporary market-impact cost against price risk from unexecuted inventory. Higher risk aversion accelerates trading, while faster orders increase impact. This simulator uses simplified exponential slice weights to calculate quadratic impact and remaining-inventory risk; it is not a calibrated solution to the full model.
- 117Machine learning
Reinforcement learning trading
Reinforcement learning frames trading as a sequence of states, actions and rewards. An agent can choose positions, then receive rewards from subsequent price changes less turnover costs. Deep Q networks and policy gradients can learn policies but may overfit simulated environments and omit market impact or non-stationarity. This small scenario scores supplied actions; it does not train an agent.
- 118Trend & time-series momentum
Fractal / Hurst-exponent regime trading
The Hurst exponent describes persistence across scales: values above 0.5 are often interpreted as trend persistence and values below 0.5 as anti-persistence or mean reversion. Finite samples, structural change, window choice and estimator bias can make H unstable; one estimate is not ground truth. This simulator selects between entered trend and reversion return scenarios using H and a neutral band.
- 119Hedging & risk management
Minimum-variance portfolio
A minimum-variance portfolio reduces risk using the covariance matrix without forecasting expected returns. Two-asset weights have a closed-form solution; long-only constraints can limit extreme weights and estimation error, but change the solution. Real multi-asset portfolios need robust covariance estimates, constraints and rebalancing costs. This illustration handles two assets only.
- 120Market making & liquidity
Order-book imbalance / limit order book prediction
Limit-order-book imbalance compares bid and ask depth; order-flow imbalance tracks how submissions, cancellations and trades affect supply/demand around the midpoint. Cont, Kukanov and Stoikov find short-horizon price changes are approximately linear in order-flow imbalance and depend on market depth. Results are highly sensitive to latency, queue position, fees and spread; this tool illustrates only an entered signal.