How to review your trades so that you can see multiple timeframes at the same time. Understanding price position is an important part of crafting your edge in the markets
Build database to organize trading data and executions. This will make pattern recognition much easier in the future similar situations.
There are 3 different tables for each market data timeframe. TradeId is used as primary key between these. All trades and their details are saved into trades table. Executions are fetched from Interactive Brokers .tlg tradelog file when processing the data.
Example query from table marketdatad
The common folder contains pieces of software that are commonly used across multiple projects. My aim is to keep this folder up-to-date so that calculations, such as the VWAP example, are always executed using the same code, ensuring consistency.
The helpers folder contains assisting functions. This folder still constitutes a major part of the program logic. For example:
HandleExecutions.py – Processes execution data from .tlg files.
ReadTlgFile.py – Reads Interactive Brokers trade log files.
FetchIBData.py – Handles fetching data from Interactive Brokers.
HandleDataFrames.py – Manages incoming bar data, which is already provided in a Pandas DataFrame structure.
config.json – Specifies paths for .tlg files and, potentially, locations for manual data entry. Also TWS API connection details are here.
Main.py – The main script where the program starts execution.
DBfunctions.py is responsible for all my database insert and fetch operations. Database is being build on top of PostgresSQL. As service providor I use Heroku