Algorithmic Stock Strategies for Backtesting

I already run my own data feeds, execution simulator and paper-trading environment, so the missing piece is a set of fresh, well-defined algorithmic strategies focused solely on equities. I’m open to any blend of trend-following, market-making or statistical-arbitrage logic—what matters is that the rules are explicit enough for me to drop straight into my existing Python back-tester.

Here’s what I need from you:
• A clear narrative of each strategy’s concept and edge.
• Precise entry, exit, position-sizing and risk parameters.
• Any indicator formulas or data transformations required.
• Clean, well-commented Python (or pseudo-code I can quickly port) that compiles without external editing.
• Brief notes on walk-forward or parameter-optimisation suggestions so I can validate results beyond the initial back-test.

I’ll run each submission through my engine and paper-trade them for at least two weeks; strategies that match their stated behaviour and stay within drawdown limits will be considered complete.

If you’ve previously deployed or researched stock algorithms and can package them in a plug-and-play format, let’s talk.