Trading Strategies - Image Classification

This repository contains all the files of the stock prediction project, related to my Machine Learning class.

Table of Contents

• Overview
• Introduction
• Trading Strategies
  • 1. Bollinger Bands
  • 2. Relative Strength Index
• Methodology
  • 1. Image Generation
    • Data Collection
    • Data Preparation
    • Signal Generation
    • Image Labeling
  • 2. Feature Engineering
    • Rescaling
    • Model Architecture
  • 3. Modeling
    • Train & Validation Split
    • Model Architecture
    • Model Selection
• Conclusion

Overview

Trading decisions have always been subjective i.e., there is no definitive answer for a decision when a trader buys/sells/holds a stock. This means that the subject-matter experts are in high demand, and we need to invest in automation to minimize human intervention. So let us explore the feasibility of building a new system that can replicate the way humans trade.

Introduction

We will be analyzing the daily data of S&P 500 Global index from 03-Jan-1983 till 18-Jun-2021.We will use this data and generate images and labels (buy/sell/hold) using some popular trading strategies. Then we use these images and train the model to classify the images and compare that with the labels that we have generated for performance evaluation.

Trading Strategies

a. Bollinger Bands

Bollinger Bands are widely used among traders. The indicator comprises an upper band, lower band and moving average line. The two trading bands are placed two standard deviations above and below the moving average (usually 20 periods) line. We use two standard deviations to capture a confidence interval of 95%. In the below image, we will make a sell decision when the actual closing index crosses the upper band and a buy decision when actual closing index falls below the lower band.

b. Relative strength index

The relative strength index (RSI) is a momentum indicator that measures the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock or other asset. The RSI is calculated using the formula [100 - 100/(1+RS)] where RS is the ratio of the Exponential Moving Average (window = 14) of all positive changes to the negative ones. An asset/stock is usually considered overbought => ready to sell when the RSI is above 70%and oversold => ready to buy when it is below 30%. In the below image, we sell at red arrows and buy at the blue ones.

Methodology

Now that we have understood about the trading strategies, let us apply these to the S&P 500 Global index data. Below are the steps I have preformed to achieve this.

1.Image Generation

We have pulled the required data from yahoo finance. This provides information about the daily open,close,high and low values for the S&P 500 index. But, our goal is to mimic the human decision; and traders look at the trend graph and make a decision by using their quantitative finance knowledge. So we now generate images using the tabular data.

Data Collection

I have used yahoo finance API to get the data. Below is the code I have used to pull the data. You can change the ticker, start and end dates to get data for different stocks.

import fix_yahoo_finance as yf
 
# Pull data from yahoo
ticker = '^GSPC'
df = yf.download(ticker, start='1983-01-01', end='2021-06-20')

Data Preparation

Below is the data structure of our tabular data. We use the Close value of each day for our whole analysis. Now we apply the Bollinger Bands & RSI strategies to the Close value and calculate the Moving Average (SMA), Bands (UB & LB) and RSI (RSI) index for each day.

Date	Open	High	Low	Close	Adj Close	Volume
14-06-21	4248.31	4255.59	4234.07	4255.15	4255.15	3612050000
15-06-21	4255.28	4257.16	4238.35	4246.59	4246.59	3578450000
16-06-21	4248.87	4251.89	4202.45	4223.7	4223.7	3722050000
17-06-21	4220.37	4232.29	4196.05	4221.86	4221.86	3952110000

Signal Generation

Based on the values of the above table, we generate signal indicators for each day. Below is the data after the signal generation. 1 --> Buy, -1 --> Sell and 0 --> Hold

Date	Close	SMA	UB	LB	RSI	BB Signal	RSI Signal
06-05-21	4201.62	4167.12	4219.78	4114.46	61.75	0	0
07-05-21	4232.60	4172.31	4229.34	4115.28	68.70	-1	0
10-05-21	4188.43	4175.33	4228.77	4121.89	52.89	0	0
11-05-21	4152.10	4175.86	4228.09	4123.62	43.41	0	0
12-05-21	4063.04	4172.78	4242.17	4103.38	28.81	1	1

Image Labeling

As we created signals for three outcomes, buy/sell/hold let us limit our decisions to either buying or selling as a first strategy. So we are going to create images and label them either buy/sell based on the BB Signal/ RSI Signal. I have created images of size 125 x 125 and labeled them as buy if the trading strategy suggests a buy or sell otherwise. The distribution of images is below for each strategy.

Strategy	Buy	Sell
Bollinger Bands	433	461
Rel Strength Index	651	1721

2.Feature Engineering

We now have the images, but we need to capture the spatial information and reduce the dimensions of the neural network that we are planning to train. I have performed the below steps to achieve these goals.

Rescaling

The image is now in the RGB format, we will rescale the image into size of 64 x 64 x 3 using the below code.

# Import the Libraries
from keras.preprocessing.image import ImageDataGenerator
# Preprocessing the Training set
train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

training_set = train_datagen.flow_from_directory('train_data_dir',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

Model Architecture

This rescaled image of size 64 x 64 x 3 is passed as the input to the below network.

• The convolution layers have 32 filters of kernel size 3 x 3 and the max pooling layers have 32 filters of kernel size 2 x 2.
• The output after the first convolution layer has the size 62 x 62 because Output Size = Input Size - Kernel Size + 1.
• The size of the output from the first max pooling layer is 31 x 31 because Output Size = InputSize / Kernel Size.
• We need to flatten the 32 filters of the images of size 14 x 14, so we add a dense layer with 6272 (14 x 14 x 32) neurons, followed by a fully connected layer with size of 128.
• The activation function used here is sigmoid for the buy or sell binary decision.

3.Modeling

Now we have the images and model architecture ready, so let us proceed with the modeling part. The important things to keep in mind here is to not overfit the data and select a robust performance metric for a classification problem like this.

Train & Validation Split

I have used stratified selection approach to split the data into training & validation. You can see the split details in the below table for a validation size of 20% of the whole data. The training data is passed into the above model architecture. We are going to validate the model on the validation data on some performance metrics.

Performance Metrics

I have selected Accuracy, Area Under the Curve and F1-Score as my model's performance metrics. After a lot of due diligence, I have achieved a validation AUC of 0.99 for the Buy/Sell classification using both BB & RSI strategies. The other model's results are below.

Conclusion

• Based on the results of the study, we can conclude that we can build a system that identifies and replicates the way humans trade.
• Since 2010, the RSI has performed incredibly well with a median of close to 4% monthly returns for buys - Source
• This model can be improved over time to tell the trader how much to buy/sell, rather than a yes/no for an investment, to maximize the returns.
• This model can be developed into a product to assist investment companies on a large scale with high computing capabilities.
• If you like the approach and want to replicate, start with the requirements.txt file to install all the required packages and run the codes after this.

Sources

• BB Image
• RSI Image