We are going to implement an End-to-End project using Support Vector Machines to live Trade For us. You Probably must have Heard of the term stock market which is known to have made the lives of thousands and to have destroyed the lives of millions. If you are not familiar with the stock market you can surf some basic Stuff about markets.
Tools and Technologies Used :
- Python
- Sklearn- Support Vector Classifier
- Yahoo Finance
- Jupyter-Notebook
- BlueShift
Step by Step Implementation
Step 1: Import the libraries
Python3
# Machine learningfrom sklearn.svm import SVCfrom sklearn.metrics import accuracy_score # For data manipulationimport pandas as pdimport numpy as np # To plotimport matplotlib.pyplot as pltplt.style.use('seaborn-darkgrid') # To ignore warningsimport warningswarnings.filterwarnings("ignore") |
Step 2: Read Stock data
We will Read the Stock Data Downloaded From Yahoo Finance Website. The Data Is stored in OHLC(Open, High, Low, Close) format in a CSV file. To read a CSV file, you can use the read_csv() method of pandas.
Syntax :
pd.read_csv(filename, index_col)
Note: We have downloaded the past 1 year data of Reliance Industries Trading In NSE from Yahoo Finance Website.
File Used:
Python3
# Read the csv file using read_csv # method of pandasdf = pd.read_csv('RELIANCE.csv')df |
Output:
Step 3: Data Preparation
The data needed to be processed before use such that the date column should act as an index to do that
Python3
# Changes The Date column as index columnsdf.index = pd.to_datetime(df['Date'])df # drop The original date columndf = df.drop(['Date'], axis='columns')df |
Output:
Step 4: Define the explanatory variables
Explanatory or independent variables are used to predict the value response variable. The X is a dataset that holds the variables which are used for prediction. The X consists of variables such as ‘Open – Close’ and ‘High – Low’. These can be understood as indicators based on which the algorithm will predict tomorrow’s trend. Feel free to add more indicators and see the performance
Python3
# Create predictor variablesdf['Open-Close'] = df.Open - df.Closedf['High-Low'] = df.High - df.Low # Store all predictor variables in a variable XX = df[['Open-Close', 'High-Low']]X.head() |
Output:
Step 5: Define the target variable
The target variable is the outcome which the machine learning model will predict based on the explanatory variables. y is a target dataset storing the correct trading signal which the machine learning algorithm will try to predict. If tomorrow’s price is greater than today’s price then we will buy the particular Stock else we will have no position in the. We will store +1 for a buy signal and 0 for a no position in y. We will use where() function from NumPy to do this.
Syntax:
np.where(condition,value_if_true,value_if_false)
Python3
# Target variablesy = np.where(df['Close'].shift(-1) > df['Close'], 1, 0)y |
Output:
Step 6: Split the data into train and test
We will split data into training and test data sets. This is done so that we can evaluate the effectiveness of the model in the test dataset
Python3
split_percentage = 0.8split = int(split_percentage*len(df)) # Train data setX_train = X[:split]y_train = y[:split] # Test data setX_test = X[split:]y_test = y[split:] |
Step 7: Support Vector Classifier (SVC)
We will use SVC() function from sklearn.svm.SVC library to create our classifier model using the fit() method on the training data set.
Python3
# Support vector classifiercls = SVC().fit(X_train, y_train) |
Step 8: Classifier accuracy
We will compute the accuracy of the algorithm on the train and test the data set by comparing the actual values of the signal with the predicted values of the signal. The function accuracy_score() will be used to calculate the accuracy.
An accuracy of 50%+ in test data suggests that the classifier model is effective.
Step 9: Strategy implementation
We will predict the signal (buy or sell) using the cls.predict() function.
Python3
df['Predicted_Signal'] = cls.predict(X) |
Calculate Daily returns
Python3
# Calculate daily returnsdf['Return'] = df.Close.pct_change() |
Calculate Strategy Returns
Python3
# Calculate strategy returnsdf['Strategy_Return'] = df.Return *df.Predicted_Signal.shift(1) |
Calculate Cumulative Returns
Python3
# Calculate Cumulutive returnsdf['Cum_Ret'] = df['Return'].cumsum()df |
Calculate Strategy Cumulative Returns
Python3
# Plot Strategy Cumulative returns df['Cum_Strategy'] = df['Strategy_Return'].cumsum()df |
Plot Strategy Returns vs Original Returns
Python3
import matplotlib.pyplot as plt%matplotlib inline plt.plot(Df['Cum_Ret'],color='red')plt.plot(Df['Cum_Strategy'],color='blue') |
Output:
As You Can See Our Strategy Seem to be Totally Outperforming the Performance of The Reliance Stock. Our Strategy(Blue Line) Provided the return of 18.87 % in the last 1 year whereas the stock of Reliance Industries (Red Line) Provides the Return of just 5.97% in the last 1 year.
Back-testing Result
1. TCS
Stock Return Over Last 1 year - 48% Strategy result - 48.9 %
2. ICICI BANK
Stock Return Over Last 1 year - 48% Strategy result - 48.9 %
Deploying Strategy To Live Market
The Strategy Coded Can be easily deployed in the live market and can also be back-tested on any number of data throughout exchanges. The deployment can be easily done Using the BlueShift Platform. It is an Interactive Platform with Live Data Feed and connections through various Brokers. You can Do Back-testing On the BlueShift platform for any Number Of Time with data from various Exchanges.
Conclusion
- The Strategy Provider Promising Returns During Live market. Currently, I have just trained the model based on previous day levels however to increase the accuracy of the model we also add various Technical Indicators for training the model such as RSI, ADX, ATR, MACD, Stochastic, and Many more.
- To Get More Accuracy in Live Market Deep Learning Proved to be Very Effective In Trading in a live market. We can Automate our Trades using Reinforcement Learning and also using Stacked LSTM which gives exponential Rise for our Strategy returns.
Note: Real Money Should not be deployed until complete Backtesting of Strategy and without promising returns by Strategy during Paper-Trading
