https://github.com/calvindd2f/trading
https://github.com/calvindd2f/trading
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/calvindd2f/trading
- Owner: Calvindd2f
- Created: 2024-05-25T16:36:54.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-17T07:15:26.000Z (almost 2 years ago)
- Last Synced: 2025-01-01T05:18:03.917Z (over 1 year ago)
- Language: Python
- Size: 835 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
To create a high-performance, low-latency crypto pump/dump detector, we need to focus on several key components:
1. *(*Data Ingestion*)*: Use REST APIs for tracking and WebSockets for real-time performance.
2. *(*Anomaly Detection*)*: Implement an algorithm to detect pumps and dumps based on price changes and volume.
3. *(*Execution*)*: Based on detections, execute trades through a rule-based workflow.
4. *(*Real-Time Performance*)*: Leverage WebSockets or SignalR for low-latency data processing and execution.
Here’s a step-by-step plan to build this:
```txt
trading_bot/
├── data/
│ └── historical_data.csv # Placeholder for historical data
├── src/
│ ├── __init__.py
│ ├── main.py
│ ├── data_processing.py
│ ├── model.py
│ └── utils.py
├── .gitignore
├── requirements.txt
└── README.md
```
## Step-by-Step Plan
1. Set Up Environment:
+ Choose a programming language (e.g., Python for simplicity and ease of use with numerous libraries, or Go for performance).
+ Install necessary libraries (e.g., requests, websocket-client for Python).
2. Data Ingestion:
+ Use REST APIs to fetch historical data.
+ Use WebSockets for real-time data updates.
3. Anomaly Detection:
+ Define what constitutes a pump and dump (e.g., significant price increase in a short period for pump, significant price decrease for dump).
+ Implement a moving average or statistical anomaly detection algorithm.
4. Trade Execution:
+ Define rules for buying and selling.
+ Use REST APIs of exchanges to place orders.
5. Real-Time Performance:
+ Use WebSockets or SignalR for real-time data streaming and decision making.
```Pseudocode
1. Initialize REST and WebSocket connections
2. Fetch historical data for baseline analysis
3. Continuously monitor real-time data via WebSockets
4. Apply anomaly detection algorithms to identify pumps and dumps
5. Execute trades based on detected anomalies and predefined rules
6. Log all transactions and events for audit and analysis
```
----------------------
### Installation
```sh
source venv/bin/activate # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt
pypy src/main.py
```
-----------------------
### Minimal Frontend
`pip install flask`
`python src/app.py`
------------------------
### Profile the code
#### Using cProfile:
```bash
python -m cProfile -o profile_output.prof src/main.py
```
#### Analyze the profile:
```python
import pstats
p = pstats.Stats('profile_output.prof')
p.sort_stats('cumulative').print_stats(10) # Print top 10 cumulative time functions
```
------------------------