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My background is working as a software engineer developing trade execution systems in C# and Python for power trading. In my free time I've built what I believe enterprise execution infrastructure should look like while also learning the quant side of trading.
I've found I'm much better at the execution infrastructure than the quant side, which got me thinking that others might be the opposite. I'm wondering if people would be interested in a platform like this if it was targeted towards solo traders/small trading firms.
I've posted info on what I've developed below. The main question I have is: **\*\*is this a solution looking for a problem or is it something people might actually be interested in?\*\***
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**## What I Built**
High-performance trade engine with comprehensive execution and risk management. Currently Binance (Spot/Futures), architected for multi-exchange support - can port to new exchanges in 1-2 weeks, or provide a common interface for users to connect their own exchange integrations.
**\*\*Performance:\*\***
\- \~0.003ms from exchange signal to derived data publication
\- 1.6ms to execute trades from 30-model ensemble
\- Sub-millisecond execution critical path
**\*\*Automated Risk Management:\*\***
\- **\*\*Strategy regulation\*\***: Programmatically toggle individual strategies between live and paper trading based on performance metrics
\- Configurable win rate thresholds (e.g., auto-disable if WR drops below 45%, re-enable above 55%)
\- Rolling PnL windows with separate enable/disable logic
\- Per-strategy monitoring and control
\- **\*\*Trade deduplication\*\***: Prevents duplicate order placement based on configurable confidence/PnL/time thresholds
\- **\*\*Position limits\*\***: Max open positions per strategy, min PnL thresholds, leverage controls
**\*\*Advanced Stop-Loss Management:\*\***
\- **\*\*Internal trailing stops\*\***: Client-side stop management for every position (not dependent on exchange)
\- **\*\*Disaster stop-loss system\*\*** (solves Binance's 5 conditional order limit):
\- Manage tight trailing stops internally (e.g., 0.5% callback) via market orders
\- Group positions with similar entry prices (e.g., within 0.3%) under shared exchange-level disaster stops (e.g., 2% hard stop)
\- Volume automatically decrements from disaster orders as positions close
\- Result: Use 200 limit orders instead of 5 conditional orders, with both tight risk control AND exchange-level failsafe
\- Fully configurable thresholds and grouping logic
\- **\*\*Local conditional orders\*\***: Upcoming feature to manage complex conditional logic client-side, working around exchange limitations
**\*\*Backtesting & Simulation:\*\***
\- Run backtests **\*\*through the actual trade engine\*\*** (not a separate simulator - closest you can get to live trading)
\- Configurable latency simulation (incoming/outgoing, default 25ms each)
\- Sophisticated slippage simulation:
\- Normal market slippage (e.g., 0.0001)
\- Gap detection with dynamic slippage
\- Maximum slippage caps
\- Parameter iteration and strategy comparison
\- Supports Tardis data, CSV tick trades, L2 order book snapshots
**\*\*Market Data & Analytics:\*\***
\- **\*\*Order book analysis\*\***: L2 depth analysis (up to 5000 levels), imbalance detection with confidence scoring, liquidity metrics, spread monitoring
\- **\*\*Multi-timeframe data\*\***: Candles and rolling candles at any granularity (1 second and up)
\- **\*\*VWAP calculation\*\***: Multiple periods, configurable buffer sizes
\- **\*\*Market regime detection\*\***: Trend analysis, volatility tracking, range detection, regime classification
**\*\*ML Integration Pipeline (Optional):\*\***
\- **\*\*Regime-aware train/test splitting\*\***: Handles non-stationary markets properly
\- Market regime clustering
\- Balanced distribution across regimes
\- Temporal bucketing to prevent leakage
\- **\*\*Automated label generation\*\***: PnL simulation on every tick trade
\- Simultaneous long/short simulation with trailing stops
\- Configurable callbacks (e.g., 0.5%, 1%, 1.5%, 2%)
\- Hold duration limits, latency-aware simulation
\- **\*\*Feature engineering\*\***: 65+ features across timeframes
\- **\*\*Ensemble orchestration\*\***: Load multiple models, consensus voting, per-group thresholds
**\*\*Performance Tracking:\*\***
\- Complete order and position history
\- Per-strategy PnL isolation with realized/unrealized tracking
\- Rolling performance windows (win rate, PnL over configurable periods)
\- Strategy performance metrics trigger automated paper trading when degraded
**\*\*Data Recording:\*\***
\- Tick trades (Tardis-compatible CSV.GZ format)
\- L2 order book snapshots at configurable intervals
\- Complete order lifecycle logging
\- PnL history with CSV export
\- Strategy predictions and signals
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**## Questions**
1. **\*\*Is there genuine interest in something like this\*\***, or are DIY solutions/existing offerings good enough for your use case?
2. **\*\*What critical features would make this a "must-have"?\*\*** What would make you think "I'd use this if it had X"?
3. **\*\*After Binance, which exchanges matter most to you?\*\*** I could add a generic interface so users can connect any exchange, but this offsets integration work to the user.
4. If you were interested in something like this what pricing would you consider reasonable? I haven't given this much thought but my initial thought would be maybe $200-400/month for self-hosted infrastructure
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If this is something people might have genuine interest in I would probably stop on the quant side of things, and focus on polishing my existing trade engine and adding some of the most requested features.