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Implementing the Alpha 101 Equity Factors from Price and Volume Data

Code Stratmill research code

Summary

This Python class implements a broad collection of formula-based equity signals using daily close, open, high, low, volume, returns, and volume-weighted average price data. Its methods translate rank, correlation, rolling-window, change, volatility, and decay operations into candidate factors. Examples include signals based on price reversals or trends, volume-price relationships, and rolling return behavior. A data preparation section assembles a multi-security panel, derives returns and a VWAP-like field, and passes those inputs to the factor calculations.

The excerpt shows implementation details rather than a trading strategy evaluation: it gives no factor definitions beyond code comments, portfolio construction rules, transaction-cost assumptions, or performance results. The visible code also includes a note that one factor may behave unexpectedly, and the displayed excerpt omits part of the implementation. Data handling, compatibility, and formula correctness therefore require review before research use. The factors are candidate signals, not evidence of predictive value or investment returns.

Key ideas

  • The class calculates many formula-based signals from daily equity price and volume fields.
  • The factors use rolling ranks, correlations, changes, sums, standard deviations, and related operations.
  • The data preparation code derives returns and a VWAP-like measure from price and trading data.
  • The excerpt provides no portfolio rules, transaction-cost analysis, backtest, or evidence of factor returns.
  • The code notes a possible issue with one factor, and the partial excerpt requires implementation review.

Tags

EquitiesFactor investingTechnical indicatorsBacktesting

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.