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Market Making Simulation & Backtest Engine
A cloud-based backtesting framework specifically engineered for market making strategies. It simulates limit order book queue position, network latency, and adverse selection to give retail traders realistic performance expectations before trading live.
Why this matters
You are an algorithmic trader trying to build a market-making strategy. You spend weeks coding a model, and your standard backtests show a beautiful, upward-trending equity curve. But the moment you deploy it live, you bleed money. Why? Because standard tools assume your limit orders get filled just because the price touched your level. In reality, faster institutional players canceled their orders, the market moved against you, and you were left holding toxic inventory. You desperately need a simulator that actually models queue position, latency, and adverse selection so you can stop losing money in live markets.
- · Built for Intermediate to advanced retail algorithmic traders who code in Python and want to deploy liquidity provision strategies..
- · Most likely monetization: SaaS subscription.
The Pain · Narrative
You are an algorithmic trader trying to build a market-making strategy. You spend weeks coding a model, and your standard backtests show a beautiful, upward-trending equity curve. But the moment you deploy it live, you bleed money. Why? Because standard tools assume your limit orders get filled just because the price touched your level. In reality, faster institutional players canceled their orders, the market moved against you, and you were left holding toxic inventory. You desperately need a simulator that actually models queue position, latency, and adverse selection so you can stop losing money in live markets.
Score Breakdown
Market Signal
Go-to-Market
Independent quantitative traders and developers building automated trading systems in Python.
~25,000 highly active retail quants globally
Hacker News launch and algorithmic trading developer communities
$99/month
15 paying users from initial beta launch in quantitative developer communities
MVP Scope · 1–2 weeks
- Define the core Python API for the backtesting framework
- Acquire a small sample of Level 2 historical tick data for one liquid crypto asset
- Build a basic limit order book matching engine in Python/Rust
- Implement a naive queue position estimator based on trading volume
- Create a simple script to visualize the simulated fills versus actual market price
- Integrate an artificial latency delay parameter into the matching engine
- Implement an adverse selection metric that penalizes fills right before large price moves
- Build a sample Avellaneda-Stoikov market making strategy to test the engine
- Develop a web landing page explaining the difference between standard backtests and this simulator
- Package the engine into a downloadable Python library with cloud-authenticated data access
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1The technical challenge of accurately simulating an exchange matching engine might prove too difficult or computationally expensive for a retail SaaS price point.
- 2Traders might not trust the simulation results until they see live proof, creating a chicken-and-egg adoption problem.
- 3The cost of licensing historical Level 2/3 data for commercial redistribution might destroy the profit margins.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
Multiple developers report that retail market making fails primarily due to inadequate backtesting. Commenters specifically highlighted the absence of realistic fill simulators, the failure to model adverse selection, and the lack of inventory caps. They noted that standard simulations look profitable but systematically fail in live environments because they ignore the reality of high-frequency trading dynamics.
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
Ready-to-paste copy based on real Reddit community language — no editing required
Headline
Market Making Simulation & Backtest Engine
Sub-headline
A cloud-based backtesting framework specifically engineered for market making strategies. It simulates limit order book queue position, network latency, and adverse selection to give retail traders realistic performance expectations before trading live.
Who It's For
For Intermediate to advanced retail algorithmic traders who code in Python and want to deploy liquidity provision strategies.
Feature List
✓ Historical Level 2 order book replay engine ✓ Configurable latency and queue position simulator ✓ Adverse selection penalty modeling ✓ Pre-built Avellaneda-Stoikov inventory management templates
Where to Validate
Share your landing page in r/r/algotrading — that's exactly where these pain points were discovered.
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