A historical backtesting and validation-research protocol for algorithmic trading strategies: it treats your entry/exit/TP-SL/capital/leverage as fixed test inputs, stress-tests them with real costs and anti-overfit gates (plateau rule, walk-forward, random control, per-year robustness), and reports a research verdict (VALIDATION GATES PASSED / FAILED / INSUFFICIENT EVIDENCE). Analysis only: never deployment, sizing, leverage, or allocation advice.
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Tired of FPL injury traps? Let this AI Agent audit your squad! Connected to the official API: 🔴 Injury Alerts: Red flags for ≤ 50% playing chance. 🟢 Form Tracker: Targets players with Form > 5.0 and easy fixtures. 📊 FDR Analysis: Calculates the best ROI for your budget. Get instant Buy/Sell/Bench recommendations based on pure data! 玩 FPL 驚買錯「玻璃腳」？呢隻 AI Agent 幫你做足資產審計！直連官方 API： 🔴 傷兵警示：出場率 ≤ 50% 自動亮紅燈 🟢 爆發評估：鎖定 Form > 5.0 兼賽程平順球員 📊 FDR 賽程對帳：計算最高 CP 值方案 即刻獲取 Buy/Sell/Bench 數據化轉會建議！
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Expert in end-to-end AI-assisted software delivery, from design engineering and design systems to shipping: design engineering, design system automation, technical documentation, spec-driven planning, polyglot code quality, systematic QA, release auditing, LLM context engineering, and Astro frontend optimization. Built for agent-agnostic workflows across the full 9-skill delivery lifecycle.
We built Autowin by putting models in a loop: discover upgrades → test them → judge them with an assembly of adversarial sub-agents — and we ran that very process on itself, iterating the kit until each piece held up under its own review. The result: from one loosely-defined need, you get back work that has been built, tested, and scored by a panel of judge sub-agents — with closure authority kept outside the model (deterministic checks + you), so an "it's done" is never just taken on faith.
Point it at your repo (or a specific change) and it scaffolds a unit-test suite for you — auto-detects your runner (Jest/Vitest, pytest, Go, XCTest, JUnit/Kotest), writes idiomatic AAA tests matching your conventions, then RUNS them and fixes test-side failures until green. It never edits your source — a real bug is reported, not papered over. Zero config.
Review AI agents, automation workflows, and project architectures for design gaps, risks, missing requirements, and improvement opportunities.
Backtests a trend-following, pullback-entry strategy on Binance USDⓈ-M perpetuals (1h) with an out-of-sample split, funding, a strict-fill check, a random-direction significance test and buy & hold comparison. For research and education only: no trading, no buy/sell recommendations, not financial advice.