# Sharpe-ratio-based risk-return strategy

Selects stocks based on recent risk-adjusted performance and dynamically rebalances the portfolio.

Canonical: https://prismaticalgos.com/compendium/algo/11540-sharpe-ratio-based-risk-return-strategy
Record ID: 11540
Catalog rank: 2326 (reading order, not performance)
Snapshot: 2026-10-31 15:46 UTC
Family: Statistical, Quantitative & Machine Learning (I)
Source: https://github.com/kexintao01-netizen/Risk-Return-Trading-Strategy-with-Python-Backtesting
Evidence label (unverified): backtest_shown

## Scope and limitations

These are machine-extracted research records, not verified trading systems. Catalog rank measures specification detail and claimed evidence, not profitability. The catalog extraction pipeline did not independently backtest these records. Its separate selected testing ledger does not validate the wider corpus. Rules, labels and parameters can be incomplete or wrong; consult the original source and its reuse terms.

No endorsement, ownership claim or source-code reuse permission is implied.

## Extraction warnings

Claim warnings: UNSIZED CLAIM: performance figures are published with no trade count, so the sample behind them cannot be judged.; UNDATED CLAIM: performance figures are published with no test period, so they cannot be checked or reproduced.

## Recorded profile

- symbols: Not specified
- timeframes: Not specified
- sessions: Not specified
- market_regime: any

## Indicators

- rolling returns: {"period": 30}; applied to: close
- rolling volatility: {"period": 30}; applied to: close

## Recorded long-entry rules

- Not specified.

## Recorded short-entry rules

- Not specified.

## Recorded exit rules

- Not specified.

## Recorded filters

- transaction costs 0.1%
- slippage 0.2%

## Risk controls

- Stop loss: total portfolio value falls below 70% of initial capital
- Take profit: null
- Position sizing: allocated according to relative Sharpe ratios