Application of Particle Swarm Optimization Algorithm with Feed-Forward Neural Network for stock forecasting

The aim of this project is to compare ability of stock forecasting between simple Feed-Forward Neural Network and Feed-Forward Neural Network which using Particle Swarm Optimization to finding best value for Feed-Forward Neural Network weight.

Technical indicator

1. EMA 5 days
2. EMA 10 days
3. MACD
4. RSI 14 days

Algorithm and technic

1. Feed-Forward Neural Network
2. Particle Swarm Optimization
3. Buy and Hold

Related work

• pyswarms Library (see detail here)

How to run this project

• create conda environment.

conda create -n myenv python=3.7

• activate your environment

conda activate myenv

• using pip to install require library

pip install -r requirements.txt

• running simulator (default simulate day is 30)

If you run the simulator in terminal you have to close the window that show the result graph for simulator next stock.

python run_simulator.py