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Saylor Innovations Trading Expectancy Calculator
x402
Saylor Innovations Trading Expectancy Calculator is a paid API for AI agents from saylorinnovations.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).
Calculates trading edge metrics — expectancy, profit factor, Kelly fraction, breakeven win rate, and expected losing streaks — from win rate and average win/loss figures
Price / call$0.01
Success—
StatusUnknown
How this service works
Trading edge from win rate and average win and loss: expectancy, profit factor, breakeven win rate, Kelly fraction and expected losing streaks.
Output
Returns a JSON object with: expectancy_per_trade (average dollar result per trade), expectancy_in_r (expectancy as a multiple of average loss), profit_factor (gross wins / gross losses), reward_to_risk ratio, kelly_fraction_percent, half_kelly_percent (clamped at zero), has_edge boolean, and projected losing streak statistics — plus a plain-language note and source attribution.
Example use cases
Evaluating a new trading strategy
“I'm backtesting a strategy with a 48% win rate, average win of $350, and average loss of $180 — can you calculate the expectancy and profit factor and tell me if it has a real edge over 200 trades?”
Sizing positions with Kelly criterion
“My algo trades with a 62% win rate, $120 average winner, and $90 average loser — what's the Kelly fraction and half-Kelly percentage I should use for position sizing?”
Checking breakeven win rate before going live
“If my average win is $250 and my average loss is $200, what win rate do I need to break even, and what's the expected longest losing streak over 150 trades?”
Technical details
Endpoint
GEThttps://saylorinnovations.com/api/trading/expectancy?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, deterministic calculation of classical trading system metrics — expectancy, profit factor, Kelly sizing, and losing streak projections — from just three parameters (win rate, avg win, avg loss). Ideal for backtesting evaluation, position-sizing decisions, and risk review workflows where you already have aggregate trade statistics and want quantitative edge assessment without building the formulas yourself.
Side effects
- Missing required query parameters (win_rate_percent, avg_win, avg_loss) returns a validation error
- win_rate_percent outside 0–100 range may produce nonsensical results
- avg_loss of zero causes division-by-zero; profit_factor returned as null
- Non-numeric string values for parameters result in parse errors
- Network or upstream errors may return HTTP 500
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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