Let’s discuss a strategy based on two technical indicators called Volume Reversal Strategy. Through this strategy, we will be able to identify the points of significant price movements accompanied by a decrease in trading volume. Let’s frame the strategy in an algorithmic note –

Condition 1: 5 day’s price change in absolute basis is greater than 100 days Standard deviation of price chance.

Condition 2: Average volume traded between last 1 to 5 days is less than Average volume traded between last 5 to 10 days

Entry Condition: If both of the above two conditions are satisfied then, we will –

  • Enter long if 5 day’s absolute price change is negative
  • Enter short if 5 day’s absolute price change is positive

Exit Condition: If any of the below two conditions are triggered then we exit.

  • Exit if a counter signal is generated.
  • Exit at the end of 5th day.

Let’s try this strategy on Vedanta using Python. First, we will import the necessary libraries. We shall be using nsePY library made by Swapnil Jariwala.

import time
start_time = time.clock()
import datetime
import csv
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from nsepy import get_history
#pip install lxml

Then our second obstacle will be making a small code to eliminate the non-trading days.

def find_no_of_working_days(start_date, end_date):
daydiff = end_date.weekday() - start_date.weekday()
days = ((end_date-start_date).days - daydiff) / 7 * 5 + min(daydiff,5) - (max(end_date.weekday() - 4, 0) % 5)
return days
no_of_work_day = 365
no_of_calender_day = no_of_work_day/5*7
end_date = datetime.datetime.now() - datetime.timedelta(days=1)
start_date = end_date - datetime.timedelta(days=no_of_calender_day)

# making sure of getting n working day(ignore sat & sun)
days = int(find_no_of_working_days(start_date, end_date))
while days < no_of_work_day:
no_of_calender_day = no_of_calender_day + (no_of_work_day - days)
start_date = end_date - datetime.timedelta(days=no_of_calender_day)
days = int(find_no_of_working_days(start_date, end_date))

Now let’s code condition 1 and condition 2 –

data['Last 5 Day Avg. Vol'] = data['Volume'].shift(1).rolling(window=5).mean() # Average volume traded in last 5 days
data['Previous-Last 5 Day Avg. Vol'] = data['Last 5 Day Avg. Vol'].shift(5) # Average volume traded between last 5 to 10 days
data['5 Day Price Change(5Day)'] = data['Close'].shift(1)-data['Close'].shift(5) # Price change for the last 5 days
pricechange_100day_roll = data.rolling(window=100)
data['STD of 100 Day Price Change(5Day)'] = pricechange_100day_roll['5 Day Price Change(5Day)'].std() #Standard deviation of price change

Then the next target is Signals column. To store the trading signals we will create a ‘signal’ column and store 0 values in it. Also, we will create two columns, c_signal, and exit, to measure the count of continuous signals and the exit criterion respectively and store 0 values in them.

data.loc[:,'signal'] = 0
data.loc[:,'c_signal'] = 0
data.loc[:,'exit'] = 0

Buy Signal and Sell Signal. Signal will be updated 1 if there is Buy signal and -1 if there is Sell Signal.

Entry Signal –

data.loc[((data['5 Day Price Change(5Day)'].abs() > data['STD of 100 Day Price Change(5Day)']) & (data['Last 5 Day Avg. Vol']< data['Previous-Last 5 Day Avg. Vol']) & (data['5 Day Price Change(5Day)']<0)), 'signal'] = 1

Exit Signal –

data.loc[((data['5 Day Price Change(5Day)'].abs() > data['STD of 100 Day Price Change(5Day)']) & (data['Last 5 Day Avg. Vol']< data['Previous-Last 5 Day Avg. Vol']) & (data['5 Day Price Change(5Day)']>0)), 'signal'] = -1

Now we will work on the entry and exit signal. We will implement the core of the strategy. Here we will check for the last entry signal, if the signal is more than 5 days old, then we exit the position, else we will continue to hold it until the signal is 5 days old or a counter signal is generated.

Let us understand what we do in each of the if statements below:

First, we check if a new signal is generated, if it is then we will assign the c_signal column equal to the new signal, and then we update the exit criterion according to the signal generated. In this case, we will check if the signal is 1 if it is so, then we will mark the exit count as 1 otherwise, we will mark it as -1.

Next, we will check if the entry signal is same as the existing position. If it is so, then we will update the exit criterion depending on the position. For example, if you are already long then your c_signal would be 1, hence it will be incremented by 1. If you are short then your c_signal is -1 and your exit will be reduced by 1.

After this, we will update the exit criterion for an already existing long trade. We check if the exit value of the c_signal is less 5 days old, if this is the case then we verify if the continuing signal is same as the previous days then we increment the exit by 1.

Just as in the above step, we will update the exit criterion for an already existing short trade.

Next, we will update the exit criterion to 0 on the fifth day after entering the trade. This is one of the conditions of the strategy and accordingly, we will close all positions on their 5th day.

		for i in range(len(data)):
			if((data.iloc[i]['signal']!=0)&(data.iloc[i]['signal']!= data.iloc[i-1]['c_signal'])) :
				if data['signal'][i] ==1:
			if((data['signal'][i]!=0)&(data['signal'][i]== data['c_signal'][i-1])) :
				if((data['exit'][i-1] < 5)&(data['exit'][i-1] > -5)):
					data.iloc[i,data.columns.get_loc('exit')]=data['exit'][i-1] + data['signal'][i]
					data.iloc[i,data.columns.get_loc('exit')]=0 + data['signal'][i]




Let’s do some calculation on the returns. Our current goal is to monitor the return of our strategy compared to the returns from the original stock price.

data['return'] = np.log(data['Close']/data['Close'].shift(1)) #Stock Returns i.e. Market Returns
data['str_return']=data['return']*data['c_signal'] #Strategy Returns
expanding_100day = data.expanding(min_periods=100)
data['cu_mar_return'] = expanding_100day['return'].sum() #Stock Returns of 100 days
data['cu_str_return'] = expanding_100day['str_return'].sum() #Strategy Returns of 100 days

To evaluate the returns of the strategy we will calculate Sharpe ratio by dividing the difference between the cumulative strategy returns and that of market returns by cumulative strategy returns.


The next and last step is to visualize the returns of the market (stock’s returns) and that of the strategy on a chart.

Yeah, the strategy did bad on Vedanta but We will keep on checking in other stocks. Also, You can try to code this strategy yourself using this tutorial in Python. Here is the complete source code. The password is unofficed.