Sayf is a trading operating system that brings hedge-fund discipline to everyday traders by codifying strategies, enforcing risk limits, and tracking performance. The platform combines a strategy library, pre-trade planning, risk engine, trade journal, and adherence scoring to replace impulsive decisions with a measurable process. It also grades execution quality by comparing fill prices to closing benchmarks for verified reporting.
Funding
Funding not disclosed
Founders
Product
Problem
Most retail traders operate without a structured process, keeping their rules in their heads and making emotionally driven sizing decisions. This leads to inconsistent execution, unplanned trades, and an inability to identify what actually drives profits or losses. Without a feedback loop, traders cannot systematically improve or distinguish good decisions from lucky outcomes.
Solution
Sayf provides a trading operating system that enforces discipline across the full trade lifecycle—from strategy definition to execution to review. Traders codify their setups, entry and exit rules, and constraints in a strategy library, then use a pre-trade planner to document thesis, entry, invalidation, and risk before entering a position. A risk engine computes max loss per trade, daily loss limits, and portfolio exposure in real time, while a trade journal records what was believed versus what happened. Performance analytics attribute results by strategy and identify mistakes like rule violations or unplanned trades, and system adherence scoring separates good decisions from lucky outcomes. The platform also grades execution quality by comparing actual fill prices to closing benchmarks, providing a transparent, verifiable track record.
Target Audience
Primary users are retail traders seeking structured discipline in their daily workflow, as well as prediction-market participants and "sharps" who want verifiable, scoreable track records for transparency and credibility.
Features
- Strategy library that codifies setups, entry rules, exits, confirmations, and constraints so every trade starts from the same playbook
- Pre-trade planning module requiring thesis, entry, invalidation, target, and risk percentage before execution
- Real-time risk engine tracking per-trade risk, daily loss limits, and portfolio exposure with automated guardrails
- Trade journal that captures planned versus actual behavior and categorizes mistake types (e.g., chased entry) and rule breaks
- Performance analytics with expectancy, profit factor, strategy attribution, and holding-period effects rather than generic dashboards
- System adherence scoring measuring rules followed, risk violations, checklist completion, and unplanned trades over time
- Execution grading using Closing Line Value (CLV) to compare fill prices against closing benchmarks, including unscoreable-row flags for honesty
- Read-only connection to trading accounts (e.g., Kalshi) with encrypted key storage and immediate key destruction option