Flowe

AI strategy preparation connected to backtesting and risk analysis

Services: AI Fintech ; AI Agents ; AI Workflow Automation
Delivery: Trading-strategy research platform.

The platform connects three areas:

• AI-assisted entry and exit rule generation.
• Historical backtesting with fee and slippage considerations.
• Exposure, drawdown, and strategy-performance analysis.

The project brief describes a strategy-development process split between manual preparation, spreadsheets, and trading tools. A trader had to turn an idea into explicit rules, test those rules, and inspect the risk before deciding whether to continue.

A generated strategy explanation cannot replace those steps. Flowe connected them in one research workflow.

The strategy builder uses market and indicator context to prepare trading rules. The research backend evaluates those rules against historical data. The dashboard presents performance and risk information so the user can compare or revise the strategy.

The documented architecture includes Python and FastAPI for research services, TimescaleDB for historical data, Redis for working state, and React for the interface.

OpenAI supports the strategy-generation layer. The exact historical model configuration is separate from the performance of the trading strategy itself.

Represent the strategy as testable rules. Entry and exit conditions need to reach the historical engine rather than remain prose.

Include execution assumptions. Fees and slippage affect the usefulness of the test, especially when comparing strategies.

Review risk beside returns. A performance view needs exposure and drawdown context.

Separate simulation from live results. A backtest is an evaluation under stated assumptions, not a claim of money earned.

The completed scope connects strategy preparation, historical testing, and review.

Project results report 90% faster strategy creation, 34% higher backtested returns, and 45% lower drawdown. The returns and drawdown figures describe historical backtesting comparisons, rather than live investment performance.

Documented stack: React; Python, FastAPI; OpenAI strategy generation; TimescaleDB; Redis; backtesting and risk-analysis modules.

Trading rules that can be tested and examined.
Flowe connects AI-assisted strategy preparation to historical testing and risk analysis, giving users the rules and evaluation outputs in one research workflow.

We built Flowe to carry a strategy idea into explicit rules, historical tests, and risk review. The model proposes the idea; the research engine evaluates it under the selected assumptions.