Explore a configurable strategy library covering moving averages, RSI, MACD, Bollinger Bands, breakouts, and multi-timeframe futures strategies.
Financial Technology / PROJECT OVERVIEW
Algorithmic Trading Platform
One workspace for market analysis, strategy research, and execution controls
An existing Python and Next.js trading application connecting Binance market data, configurable strategies, historical backtesting, and operational dashboards. The project includes paper-trading workflows, futures account monitoring, MetaTrader 5 integration, and optional AI-assisted market analysis.
01 / INSIDE THE PRODUCT
What it brings together.
Engineering case study based on the existing application and source code.
Run strategies in dry-run mode, inspect their state and signals, and manage workers from an operational dashboard.
Replay historical candles through the backtesting engine and review return, win rate, profit factor, drawdown, Sharpe ratio, and expectancy.
Monitor Binance futures account data, positions, orders, fees, and strategy-level profit and loss.
Control live execution through explicit configuration, account preflight checks, order-approval support, and configurable daily loss and commission limits.
Connect supported MetaTrader 5 workflows alongside the Binance integration.
Use optional Ollama and Gemini integrations for market context and AI-assisted analysis.
Expose strategy, account, and research workflows through MCP tooling for connected operator interfaces.
An existing product, documented from source
This case study describes the Binance application already built for strategy research and trading operations. Its Next.js interface includes dedicated strategy, backtesting, prediction, MetaTrader 5, and MCP sections. A FastAPI service coordinates the underlying Python engine and broker adapters.
From market data to a testable strategy
Strategies combine configurable indicators with market context such as candle patterns, support and resistance, order-book pressure, and taker flow. The historical replay engine feeds candles to strategy implementations one step at a time, supporting research before a strategy is considered for execution.
Paper workflows and execution controls
Strategy start requests default to dry-run mode. Live execution requires an explicit configuration switch and a successful account preflight. Configurable commission and daily loss limits, worker status, and order-approval support make execution behavior visible to the operator.
A modular application architecture
React and TypeScript provide the Next.js interface, with services and view models organizing client workflows. Python modules separate strategy execution, technical indicators, historical testing, account access, and optional AI providers. FastAPI supplies the application endpoints, while MCP adapters expose selected workflows to connected tools.
Visibility into results and operations
The platform distinguishes research metrics from live account state. Strategy monitoring, signals, fees, position data, and profit-and-loss views help investigate how a strategy behaves. Backtest outputs describe a historical simulation; this project page makes no claims about future returns.
Built with the right tools.
PythonFastAPINext.jsReactTypeScriptTailwind CSSBinance APIpandasMetaTrader 5OllamaMCP
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