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Stat-Arb Cost Calibration API
Offer an API and dashboard that estimates realistic transaction costs for pair and spread strategies using instrument-level spread, depth, borrow, and order-type assumptions. The product helps traders replace blunt basis-point estimates with dynamic live-calibrated cost curves.
Why this matters
You can accept mediocre alpha, but you cannot survive imaginary cost assumptions. When your strategy only earns a narrow margin before trading frictions, every hidden basis point matters. The common workaround is to hardcode a simple fee estimate and hope reality is close enough, but that breaks down when each spread trade effectively requires four fills and liquidity is inconsistent across symbols and times of day. You need a cost layer that updates by instrument and order style, then feeds back into your backtest and paper-trading results. Without that, you are optimizing on a distorted objective and discovering the truth only after going live.
- · Built for Quant hobbyists, systematic retail traders, and small funds that backtest high-turnover equity or ETF strategies with fragile margins..
- · Most likely monetization: SaaS subscription.
The Pain · Narrative
You can accept mediocre alpha, but you cannot survive imaginary cost assumptions. When your strategy only earns a narrow margin before trading frictions, every hidden basis point matters. The common workaround is to hardcode a simple fee estimate and hope reality is close enough, but that breaks down when each spread trade effectively requires four fills and liquidity is inconsistent across symbols and times of day. You need a cost layer that updates by instrument and order style, then feeds back into your backtest and paper-trading results. Without that, you are optimizing on a distorted objective and discovering the truth only after going live.
Score Breakdown
Market Signal
Go-to-Market
Python-based quants who already run custom backtests and want a drop-in API for realistic transaction-cost assumptions.
~20K-50K active global users
SEO long-tail
$79/month
100 API signups and 15 active weekly users calling the cost endpoint from backtests within 30 days
MVP Scope · 1–2 weeks
- Define a cost-estimation schema with symbol, side, size, order type, and time inputs
- Build a rules-based cost engine using spread and volume proxies
- Create a Python SDK that plugs into backtests with one function call
- Add a simple web dashboard for comparing assumed versus modeled costs
- Prepare sample notebooks for ETF and equity pair-trading use cases
- Add one market data integration for intraday quote history
- Implement portfolio-level cost reporting across all trades in a backtest
- Create an upload flow for paper-trade fills to calibrate model error
- Ship versioned APIs for static estimate mode and calibrated estimate mode
- Publish benchmark case studies showing how cost realism changes apparent Sharpe and net edge
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1The product could be seen as too simplistic if it lacks full order-book information, reducing credibility among sophisticated users.
- 2Many target users may expect low pricing because they perceive cost modeling as a small component rather than a standalone product.
- 3The value is strongest in fragile high-turnover strategies, which narrows the segment if expansion into other strategy types is slow.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
Several comments centered on the gap between assumed and realized trading costs. Participants highlighted that small monthly returns can vanish when actual round-trip costs are higher than modeled, especially in thin products and two-leg trades. This creates a clear opportunity for dynamic cost estimation that plugs directly into research and paper-trading workflows.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Build
Strong demand signals detected. Real pain, real willingness to pay — start building an MVP.
Landing Page Copy Kit
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Headline
Stat-Arb Cost Calibration API
Sub-headline
Offer an API and dashboard that estimates realistic transaction costs for pair and spread strategies using instrument-level spread, depth, borrow, and order-type assumptions. The product helps traders replace blunt basis-point estimates with dynamic live-calibrated cost curves.
Who It's For
For Quant hobbyists, systematic retail traders, and small funds that backtest high-turnover equity or ETF strategies with fragile margins.
Feature List
✓ Instrument-specific round-trip cost estimates ✓ Backtest plug-in API for dynamic transaction cost modeling ✓ Post-trade calibration using paper or live fill logs
Where to Validate
Share your landing page in r/r/algotrading — that's exactly where these pain points were discovered.
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