Formula, in AI in Markets
Cost Per Validated Trade Formula
Cost per validated trade divides the total LLM and validation spend by the number of trade signals that actually clear validation. It captures the real unit economics of an AI trading agent: every signal the model proposes costs tokens, but only the ones that pass the checks become tradeable, so the rejects inflate the cost of each usable trade. It must stay well below the edge per trade.
4 variables. Published May 26, 2026. By AI Fin Hub Research.
CostPerValidatedTrade = (N_signals x CostPerSignal) / N_validated
N_validated = N_signals x ValidationPassRate Variables
N_signals
Signals generated
Total number of candidate trade signals the agent produces, each consuming an LLM call (or several). Every signal costs money whether or not it is ultimately traded.
CostPerSignal
Cost per signal
The LLM and tooling cost to generate and check one candidate signal, including prompt, generation, and any validation calls. Computed from the cost-per-1K-tokens formula across the full pipeline for one signal.
ValidationPassRate
Validation pass rate
Fraction of generated signals that survive validation (sanity checks, risk gates, confidence thresholds). A low pass rate means most spend goes to rejected signals, raising the cost of each validated trade.
N_validated
Validated trades
Number of signals that pass validation and become actual trades, equal to signals times the pass rate. This is the denominator that matters for unit economics.
Step by step
- 1
Count signals generated and compute the per-signal LLM cost.
The agent generates 1,000 signals a day at 0.08 each in LLM cost.
- 2
Determine the validation pass rate from the pipeline logs.
Only 12% of signals clear the risk and confidence gates.
- 3
Compute validated trades as signals times pass rate.
1,000 x 0.12 = 120 validated trades.
- 4
Divide total cost by validated trades, then compare to the expected edge per trade.
Total cost 1,000 x 0.08 = 80; cost per validated trade = 80 / 120 = 0.667.
Worked example
AI signal agent with a strict validation gate
Signals generated
1,000/day
Cost per signal
0.08
Validation pass rate
12%
Total cost = 1,000 x 0.08 = 80. Validated trades = 1,000 x 0.12 = 120. Cost per validated trade = 80 / 120 = 0.667. Equivalently, CostPerSignal / PassRate = 0.08 / 0.12 = 0.667.
About 0.67 per validated trade, more than 8x the 0.08 raw cost per signal, because 88% of generations are discarded. This number is the gate on viability: if the strategy's expected profit per trade is, say, 2.00, an LLM cost of 0.67 leaves room, but if a tighter gate drops the pass rate to 3%, cost per validated trade jumps to 0.08/0.03 = 2.67 and the agent loses money on each trade before slippage. Raise the pass rate (better prompting, cheaper pre-filtering) or cut per-signal cost.
Common variations
Run the numbers next
- Calculators Token-Cost Optimizer
Compute the dollar cost of a trading research loop across Claude, GPT, and Gemini. Prompt length × model × retry × call volume → cost per idea and per.
- Comparators Model Selector for Finance
Input task, latency budget, cost budget, context size, and quality sensitivity; get ranked model recommendations with rationale — grounded in published.
Sources
- Anthropic Claude Pricing , Anthropic
- OpenAI API Pricing , OpenAI
Keep the topic connected
- Formula Cost Per 1K Tokens Formula
The cost-per-1K-tokens formula: input tokens times input price plus output tokens times output price. Why output dominates LLM cost, with an example.
- Formula Prompt Cache Break-Even Formula
The prompt-cache break-even formula: how many reuses of a cached prefix repay its write premium. When caching pays off for LLM workloads.
- Learn Agent-Cost Envelope
The agent-cost envelope: the loop of (calls × tokens × retries × model_price) that determines the dollar cost of an LLM-driven trading agent per decision.
- Formula Expectancy Formula
The expectancy formula: win rate times average win minus loss rate times average loss. The expected profit per trade in R-multiples, with an example.