Asset Trading

Algorithm

Overview

Timeline


Nov 2021 - Present

Role


Software Developer

Scope


Coding & API's

Financial Data

BACKGROUND


Nowadays trading algorithms are widely used within financial trading of stocks within the global stock markets. Whether it be algorithms to fill orders effectively for someone looking to acquire/dump a large amount of shares, or to buy and sell stock at programmed intervals to profit throughout each trading day. This simple algorithm experiment is the latter, combining my interest of capital markets with Python and JSON API's.

TECHNICAL OVERVIEW


The algorithm is programmed in Python and fetches financial market data as JSON through the FMP API. As of now the algorithm is pretty basic and is a base foundation for what I hope to turn into a consistent high profit rate algo.


Currently, I am still learning about how to potentially incorporate neural networks within the project.

FMP


TECHNICAL TRADING INDICATORS


The algorithm currently requests technical trading indicator values which can be used as a parameter to make a trade decision.


Technical Indicators:
  • RSI - Relative Strength Index
  • SMA - Simple Moving Average
  • EMA - Exponential Moving Average
  • WMA, DEMA, TEMA, Williams, ADX, Standard Deviation...

Simple Example:

If RSI dips below 30, execute a buy order of X amount of stock, If RSI rises above 70, execute a sell order of X amount of stock.



Financial Modeling Prep


FMP - Financial Modeling Prep, is the API platform in which provides the market data used in the algorithm.


FMP has a wide variety of data able to be incorporated within algorithms and financial applications, a few examples of data fetched in this algorithm are:


  • Realtime Asset Prices
  • Technical Indicators
  • Historical Prices and Values
  • Company Profile Data