TTM Squeeze Stocks Scanner Using Python

In this tutorial, I will build a TTM Squeeze scanner using Python and Mongodb

Let us first import the necessary packages for pandas and pymongo.

In [1]:

import pymongo
from pymongo import MongoClient
import datetime
import pandas as pd

I have stocks data for all stocks in mongodb.
Let us initiate the mongo DB Connection.

In [ ]:

client = MongoClient('mongodb://localhost:27017')
db = client['stocksdb']

Let us look at our stock document. Document is similar to a row in MySQL.

In [4]:

db.stock_data.find_one()

Out[4]:

{'_id': ObjectId('6202f99a80d4ed2d9c536156'),
 'date': datetime.datetime(2020, 1, 2, 0, 0),
 'open': 21.86,
 'high': 21.86,
 'low': 21.315,
 'close': 21.42,
 'adjusted_close': 21.3733,
 'volume': 3062541,
 'ticker': 'AA',
 'perchange': None}

From the collection/table in mongodb, we can retrieve the data by date. For that we need to provide from and to date.

Let us get the data for the last 100 days. To get this data, we will filter the data by date from "today" to 100 days back.

Let us get the today's date in Python datetime format.

In [59]:

today = datetime.datetime.strptime(datetime.datetime.today().strftime('%Y-%m-%d'),'%Y-%m-%d')

Below, i am extracting the date which is 100 days ago from mongoDB.

In [22]:

ticker='AAPL'
date_100_days_ago = db.stock_data.find({'ticker':ticker,'date':{"$lte":today}}).sort([('date',pymongo.DESCENDING)]).limit(100)[99]['date']

Let us get the data for the ticker 'AAPL' using above dates.

In [54]:

fdocs = db.stock_data.find({'ticker':ticker,"$and": [ { 'date': {"$lte": today} }, { 'date': { "$gte": date_100_days_ago } } ] },{'_id':0,'adjusted_close':0,'volume':0,'ticker':0,'perchange':0}).sort([('_id',pymongo.DESCENDING)])
df = pd.DataFrame(list(fdocs))
df = df[::-1]

In [55]:

df.head(1)

Out[55]:

	date	open	high	low	close
0	2022-11-16	149.13	149.87	147.29	148.79

(Optional) - Let us rename the columns to make first lettter upper case.

In [12]:

df.rename(columns = {'date':'Date','open':'Open','high':'High','low':'Low','close':'Close'},inplace=True)

Let us wrap the above code inside a function so that we can run it for any stock.

In [28]:

def get_stocks_dataframe(ticker):
    fdocs = db.stock_data.find({'ticker':ticker,"$and": [ { 'date': {"$lte": today} }, { 'date': { "$gte": date_100_days_ago } } ] },{'_id':0,'adjusted_close':0,'volume':0,'ticker':0,'perchange':0}).sort([('_id',pymongo.DESCENDING)])
    df = pd.DataFrame(list(fdocs))
    df.rename(columns = {'date':'Date','open':'Open','high':'High','low':'Low','close':'Close'},inplace=True)
    df = df[::-1]
    return(df)

Lastly, the below snippet of code will calculate the bollinger band, keltner channel and prints the stocks which are in and out of ttm squeeze.
Note the criteria to find "out of squeeze" stocks can be changed in the following code...
df.iloc[-2]['squeeze_on'] and not df.iloc[-1]['squeeze_on'].
In above code snippet, I am checking whether the previous day, stock was in "in squeeze".

In [ ]:

def in_squeeze(df):
    return df['lband'] > df['lkeltner'] and df['uband'] < df['ukeltner']

dataframes = {}

#loop through all the stocks
for doc in db.stock_data.find():
    symbol = doc['symbol']
    #get dataframe from mongoDB
    df = get_stocks_dataframe(symbol)
    try:
        df['20sma'] = df['Close'].rolling(window=20).mean()
        df['stddev'] = df['Close'].rolling(window=20).std()
        #lower band
        df['lband'] = df['20sma'] - (2 * df['stddev'])
        #upper band
        df['uband'] = df['20sma'] + (2 * df['stddev'])

        df['TR'] = abs(df['High'] - df['Low'])
        df['ATR'] = df['TR'].rolling(window=20).mean()

        #lower keltner channel
        df['lkeltner'] = df['20sma'] - (df['ATR'] * 1.5)
        #upper keltner channel
        df['ukeltner'] = df['20sma'] + (df['ATR'] * 1.5)

        df['squeeze_on'] = df.apply(in_squeeze, axis=1)
        
        if df.iloc[-2]['squeeze_on'] and not df.iloc[-1]['squeeze_on']:
            print(symbol,"out of squeeze")
        if df.iloc[-1]['squeeze_on']:
            print(symbol,"in squeeze")
    except:
        continue
        
    dataframes[symbol] = df

TTM Squeeze Stocks Scanner Using Python

Related Notebooks