Turn your trading ideas into optimized, autonomous profit alpha trading analytics engines
Helping serious traders turn ideas into rigorously tested, production-ready systematic strategies.
Interactive Strategy Visualization
Trades are plotted directly on price data, allowing you to see exactly where entries, exits, stop events, and take-profits occur.
Cumulative returns, per-trade performance, and supporting metrics are displayed alongside execution markers, creating a direct link between strategy logic and real outcomes.
This makes it easy to:
Validate that signals are triggering as intended
Confirm execution behavior aligns with backtest assumptions
Spot logic errors or unexpected trade patterns
Identify structural weaknesses in the strategy design
Diagnose performance anomalies quickly
Because results are visual and interactive, issues that might be hidden in raw statistics become immediately apparent.
The goal is not just performance measurement — it’s clarity.
You can see how the strategy thinks, how it acts, and how it performs, all in one view.
Detailed Trial by Trial Results
Trials can be reviewed in a sortable table, allowing you to assess performance across a wide range of metrics — including return characteristics, risk statistics, win rates, drawdowns, parameter values, and timestamps — all in one place.
You can:
Sort by any performance metric
Compare parameter configurations side-by-side
Inspect iteration counts and optimization metadata
Quickly identify top-performing or unstable regions of parameter space
This makes it easy to understand not just which trial performed best, but why — and how different parameter combinations influence outcomes across multiple dimensions.
When deeper analysis is required, results can be exported to CSV for further inspection, reporting, or integration into external research workflows.
Clear visibility. Structured comparison. Portable results.
ML Model Training
The framework supports a wide range of models — from classical algorithms to advanced neural networks — with structured training, reproducible validation, and scalable hyperparameter optimization built in.
Experiments are tracked, comparable, and versioned, making it easy to iterate without losing control or introducing drift.
Once validated, model instances integrate directly into the strategy layer. Predictions become signals inside the same unified architecture used for rule-based systems — no duplicated logic, no fragile handoffs between research and live trading.
Train. Optimize. Validate. Deploy.
All within a single, consistent system.
Built-in Queue System
Jobs are intelligently scheduled and processed based on priority and available resources, ensuring the system runs at maximum efficiency while remaining stable. You can monitor status in real time, track progress, and review completed or failed runs with full transparency.
Scale experimentation confidently — the platform handles the orchestration.
High Level Results
You can see how changes in one parameter ripple through others — and how those combinations drive the objective outcome. High-performing configurations stand out immediately, making it easier to identify stable regions, spot overfitting patterns, and understand trade-offs between variables.
It transforms optimization from guesswork into structured insight.
What We Do
At Quantforge Software, we specialize in building, backtesting, and optimizing trading strategies using robust quantitative engineering and a scalable research and execution framework.
Our platform supports traditional rule-based systems as well as the integration and training of machine learning models — including neural networks and advanced predictive pipelines — when your strategy design calls for it. Optimization is driven by systematic hyperparameter search techniques (leveraging Optuna search algorithms on distributed Ray Tune infrastructure), enabling efficient exploration of parameter space with a strong emphasis on out-of-sample robustness.
The focus is not curve-fitting — it’s structured, data-driven refinement and validation.
Once validated, strategies can be deployed into automated execution environments, allowing your strongest configurations to operate with discipline and consistency — giving you back your time while maintaining full transparency and control.
How We Do It
Our approach is built on years of hands-on quantitative development in Python, systematic strategy research, and real-world deployment for clients transitioning from discretionary trading to automation.
A common failure point in algorithmic trading is architectural: traders build one version of a strategy for backtesting, then rewrite it separately for live execution. Over time, this split creates drift — small logic mismatches, fill assumptions, parameter inconsistencies, and maintenance overhead that erode confidence and performance.
We eliminate that problem at the structural level.
Our framework derives execution signals directly from the validated research layer. The same strategy logic that powers backtests is used to generate live trading signals — meaning there is a single source of truth.
There is no duplicated logic.
No parallel implementations.
No silent divergence between research and execution.
The framework itself evolved from years of building and optimizing strategies using robust quantitative tooling, including vectorbtpro and a broader Python-based research stack. It was designed specifically to solve the recurring friction traders face when moving from manual processes to systematic automation:
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Translating discretionary ideas into structured, testable logic
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Running disciplined backtests without lookahead bias
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Performing controlled parameter optimization
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Maintaining reproducibility across experiments
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Deploying validated strategies without rewriting core logic
The result is a unified workflow:
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Formalize the strategy into clean, testable code
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Validate through structured backtesting and robustness testing
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Optimize with controlled, reproducible experimentation
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Deploy the exact same logic into automated execution
One framework.
One strategy definition.
One source of truth.
This ensures consistency, transparency, and long-term maintainability — not just automation.
Pricing
Framework Deployment & Hosting
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Initial deployment
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Framework updates & maintenance
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Secure hosting, performance & uptime monitoring
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Ongoing technical support
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1 hour system walkthrough
Strategy Research, Development & Support
$90 Hourly
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Direct support from experienced quantitative developers
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Strategy design & implementation
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Performance analysis & refinement
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System support & training
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Trading support & analysis