Emil
10.4K posts

Emil
@cuzmane
Equities / Options trader. In the business since 2000 | Tweets are for entertainment and def. not investment advice










📈Stock Allocation - When Scaling-In Beats "Going Big" Recently I've been working on a systematic algorithm* backtested over 6.2 years (2020 to now) across stocks with a current market cap of $1B+ The backtest generated over 18,000 trade signals with an average return of 11.95% per trade. The question was "what allocation % per trade yields the best results?" It turned out 1–2% was the optimum. 1% produced a CAGR of 64.9% — worst year 2022 (+8.6%), best year 2025 (+100.6%). Max drawdown was 5.9% with a Sharpe of 1.76. $100,000 became $2,189,300. Win Rate: ~49–54% Avg Win: ~+25% Avg Loss: ~−6.2% Avg hold: 23.2 days 23 trades returned +500%+. Several exceeded +1,000%. Why does small sizing win? At 10% (10 slots), the sim skips most trades. At 1–2% (50–100 slots), you're actually there when the big ones hit. Admittedly this is WIP — there may be errors in the data. The large winners have been verified manually and with AI, and the raw trade data has been shared with colleagues for peer review. Next step: run it live with real capital through the rest of 2026 and track out-of-sample results. * Algorithm is written and executed in Python. The resulting trades are fed into Claude Pro (Sonnet 4.6) to run Monte-Carlo simulations, walk-forward portfolio analysis etc.























