Summary
The firm spends time on manual market analysis, spreadsheet handling and order placement.
The solution became Python trading software that connects market data, strategy logic, spreadsheets and broker accounts.
The software is made to reduce manual trading work, repeated data handling and avoidable execution effort.
Client story
The client is an algorithmic trading firm from Poland. It runs live, backtested and high-frequency strategies through brokerage APIs and works with stock and options trading.
The team handles candle data, trading inputs and buy and sell orders across brokerage accounts. Traders and trading businesses need to replace manual market-data analysis, trade-data handling and order placement with automated systems.
The request was simple: automate the trading workflow in Python, connect it with Interactive Brokers or a Shoonya Broker Account where required, and validate strategies through backtesting. The firm also needed Excel data to move into the process and return as updated trading data after execution.
The solution was shaped as connected screens for interval setup, candle analysis, live monitoring, Excel data and backtesting. Strategy fields, order types, broker status and trading logs were kept visible so the firm could manage the process around its own rules.
The client asked for
- Validate strategies through backtesting and provide requested guidance or documentation.
- Build the trading automation in Python, with Java or C++ alternatives where needed.
- Connect Interactive Brokers or Shoonya Broker Account where required.
- Read Excel trading inputs and update the data after execution.
Why ready-made software did not fit
Ready-made brokerage platforms cover buying, selling and managing financial assets, but their descriptions do not combine this firm’s spreadsheet inputs, configurable strategy logic, backtesting and broker execution in one tailored process. The solution was designed to keep these parts together and align them with the firm’s own rules, while documentation and training support independent use.
Project overview
The solution was designed around a Python trading application that connects market data, strategy logic, spreadsheet inputs and broker accounts. The structure keeps the main stages in one place: prepare data, review candles, validate a strategy, execute orders and monitor positions.
To handle trading inputs, an Excel workspace was provided. It reads spreadsheet data, shows candle data and marks the updated trading data produced after execution.
The trading screen brings together selectable intervals, strategy parameters and buy and sell criteria. Broker connection status and market, limit or stop order types remain available in the same screen.
The application places orders through supported broker connections and keeps execution information available for monitoring.
Special attention was paid to strategy validation. The backtesting screen uses historical candle data, strategy parameters and results tables so the firm can compare a strategy before using it for live trading.
The system was planned as modules, leaving room for documentation, setup guidance, post-delivery support and later additions such as local processing or live-market debugging.
Key features delivered
- Automated trading through broker accounts
- Market, limit and stop orders
- Candle data fetching at selectable intervals
- Configurable strategies and buy/sell criteria
- Excel trading data import and update
- Strategy backtesting with historical candle data
- Live order, position and alert monitoring
- Broker account connection status
- Python-based trading automation
