https://github.com/mrinalxdev/flowtrade
A algorithm for trading built in golang
https://github.com/mrinalxdev/flowtrade
go
Last synced: about 1 month ago
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A algorithm for trading built in golang
- Host: GitHub
- URL: https://github.com/mrinalxdev/flowtrade
- Owner: mrinalxdev
- Created: 2023-12-16T20:08:13.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-28T22:42:29.000Z (about 2 years ago)
- Last Synced: 2025-01-20T17:21:58.484Z (about 1 year ago)
- Topics: go
- Language: Go
- Homepage:
- Size: 3.38 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: Readme.md
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README
>Test algorithmic trading strategies in a risk-free Golang simulator.
```Note the prices inlist are not the real ones```
TradeFlow is a comprehensive financial simulation project developed in Golang that allows users to model and test algorithmic trading strategies in a controlled environment. The project incorporates various components, including a trading algorithm, portfolio management, market data simulation, and utilities for random data generation.
## Key Features of the algorithm
1. Trading Algorithm : The simulator includes a robust trading algorithm that executes buy and sell orders based on user-defined parameters. It considers factors such as order size, risk per trade, trailing stop loss, and simple moving average to make informed trading decisions.
2. Portfolio Management : The portfolio management module tracks the user's portfolio, managing cash, open positions, and overall risk exposure. It allows for deposits, withdrawals, and provides a historical record of trades.
3. Market Data Simulation: Simulated market data introduces realistic price fluctuations for various symbols, enabling users to assess the performance of their trading algorithms under diverse market conditions.
4. Utility Functions: The utility package includes essential functions such as generating unique trade IDs, random prices, and other auxiliary operations crucial for the simulation.
## Advantages in the Real World:
- Risk-Free Strategy Testing: Traders and financial analysts can use this simulator to test and refine their algorithmic trading strategies in a risk-free environment before deploying them in live markets. This can potentially save significant financial losses associated with untested strategies.
- Educational Tool: The simulator serves as an educational tool for students, developers, and anyone interested in algorithmic trading. Users can gain hands-on experience in understanding market dynamics, risk management, and the implementation of trading algorithms.
- Portfolio Management Practice: Traders can experiment with different portfolio management strategies, optimize risk exposure, and learn how to balance positions effectively. This practical experience contributes to more informed decision-making in real-world trading scenarios.
## Use Cases:
- Algorithmic Trading Development: Quantitative analysts and algorithmic traders can use the simulator to develop, test, and optimize their trading algorithms before deploying them in live markets.
- Financial Education: Educational institutions and online learning platforms can leverage the simulator to teach students and enthusiasts about algorithmic trading concepts, market dynamics, and risk management.
- Trader Skill Enhancement: Individual traders can use the simulator to enhance their trading skills, experiment with various strategies, and gain confidence in their decision-making abilities without risking real capital.
- Investment Strategy Validation: Institutional investors and hedge funds can use the simulator to validate and fine-tune their investment strategies, ensuring that their algorithms align with their risk tolerance and overall investment objectives.