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Implementing the Squeeze Momentum Indicator in Python
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yongghongg
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Squeeze_Momentum_Indicator_LazyBear_Demo.ipynb
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Squeeze_Momentum_Indicator_LazyBear_Demo.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Squeeze-Momentum-Indicator-LazyBear-Demo.ipynb",
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"provenance": [],
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"collapsed_sections": [],
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"authorship_tag": "ABX9TyMPOlxSbHiQSr+XPYFnINuh",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/yongghongg/stock-screener/blob/main/Squeeze_Momentum_Indicator_LazyBear_Demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "nKgeIyzc7IMw"
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},
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"source": [
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"A demo Colab Notebook for my Medium article 'Implementing the Squeeze Momentum Indicator in Python'"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "jZjYphw06_5Y"
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},
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"source": [
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"!pip install yfinance\n",
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"!pip install bs4\n",
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"!pip install requests\n",
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"!pip install ta\n",
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"!pip install finta\n",
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"!pip install mplfinance\n",
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"import pandas as pd\n",
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"import requests\n",
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"from datetime import datetime\n",
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"import yfinance as yf\n",
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"from bs4 import BeautifulSoup\n",
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"import numpy as np\n",
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"import math\n",
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"import mplfinance as mpf"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "CFmF-TFE0M3I"
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},
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"source": [
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"screened_list = []\n",
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"stock_list = ['AAPL','TSLA','MSFT','AMZN']\n",
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"for stock_code in stock_list:\n",
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" # get stock prices\n",
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" df = yf.download(stock_code, start='2020-01-01', threads= False)\n",
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"\n",
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" # parameter setup\n",
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" length = 20\n",
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" mult = 2\n",
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" length_KC = 20\n",
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" mult_KC = 1.5\n",
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"\n",
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" # calculate BB\n",
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" m_avg = df['Close'].rolling(window=length).mean()\n",
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" m_std = df['Close'].rolling(window=length).std(ddof=0)\n",
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" df['upper_BB'] = m_avg + mult * m_std\n",
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" df['lower_BB'] = m_avg - mult * m_std\n",
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"\n",
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" # calculate true range\n",
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" df['tr0'] = abs(df[\"High\"] - df[\"Low\"])\n",
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" df['tr1'] = abs(df[\"High\"] - df[\"Close\"].shift())\n",
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" df['tr2'] = abs(df[\"Low\"] - df[\"Close\"].shift())\n",
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" df['tr'] = df[['tr0', 'tr1', 'tr2']].max(axis=1)\n",
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"\n",
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" # calculate KC\n",
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" range_ma = df['tr'].rolling(window=length_KC).mean()\n",
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" df['upper_KC'] = m_avg + range_ma * mult_KC\n",
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" df['lower_KC'] = m_avg - range_ma * mult_KC\n",
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"\n",
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" # calculate bar value\n",
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" highest = df['High'].rolling(window = length_KC).max()\n",
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" lowest = df['Low'].rolling(window = length_KC).min()\n",
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" m1 = (highest + lowest)/2\n",
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" df['value'] = (df['Close'] - (m1 + m_avg)/2)\n",
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" fit_y = np.array(range(0,length_KC))\n",
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" df['value'] = df['value'].rolling(window = length_KC).apply(lambda x: \n",
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" np.polyfit(fit_y, x, 1)[0] * (length_KC-1) + \n",
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" np.polyfit(fit_y, x, 1)[1], raw=True)\n",
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"\n",
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" # check for 'squeeze'\n",
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" df['squeeze_on'] = (df['lower_BB'] > df['lower_KC']) & (df['upper_BB'] < df['upper_KC'])\n",
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" df['squeeze_off'] = (df['lower_BB'] < df['lower_KC']) & (df['upper_BB'] > df['upper_KC'])\n",
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"\n",
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" # buying window for long position:\n",
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" # 1. black cross becomes gray (the squeeze is released)\n",
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" long_cond1 = (df['squeeze_off'][-2] == False) & (df['squeeze_off'][-1] == True) \n",
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" # 2. bar value is positive => the bar is light green k\n",
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" long_cond2 = df['value'][-1] > 0\n",
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" enter_long = long_cond1 and long_cond2\n",
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"\n",
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" # buying window for short position:\n",
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" # 1. black cross becomes gray (the squeeze is released)\n",
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" short_cond1 = (df['squeeze_off'][-2] == False) & (df['squeeze_off'][-1] == True) \n",
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" # 2. bar value is negative => the bar is light red \n",
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" short_cond2 = df['value'][-1] < 0\n",
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" enter_short = short_cond1 and short_cond2\n",
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"\n",
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" if enter_long | enter_short:\n",
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" screened_list.append(stock_code)\n",
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"\n",
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"if screened_list:\n",
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" print(screened_list)\n",
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"else:\n",
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" print('No stock fits the Squeeze Momentum Indicator entry requirement')"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "ZeSijaFbbt1w"
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},
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"source": [
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"# to make the visualization better by only taking the last 100 rows of data\n",
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"df = df[-100:]\n",
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"\n",
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"# extract only ['Open', 'High', 'Close', 'Low'] from df\n",
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"ohcl = df[['Open', 'High', 'Close', 'Low']]\n",
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"\n",
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"# add colors for the 'value bar'\n",
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"colors = []\n",
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"for ind, val in enumerate(df['value']):\n",
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" if val >= 0:\n",
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" color = 'green'\n",
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" if val > df['value'][ind-1]:\n",
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" color = 'lime'\n",
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" else:\n",
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" color = 'maroon'\n",
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" if val < df['value'][ind-1]:\n",
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" color='red'\n",
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" colors.append(color)\n",
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" \n",
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"# add 2 subplots: 1. bars, 2. crosses\n",
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"apds = [mpf.make_addplot(df['value'], panel=1, type='bar', color=colors, alpha=0.8, secondary_y=False),\n",
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" mpf.make_addplot([0] * len(df), panel=1, type='scatter', marker='x', markersize=50, color=['gray' if s else 'black' for s in df['squeeze_off']], secondary_y=False)]\n",
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"\n",
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"# plot ohcl with subplots\n",
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"fig, axes = mpf.plot(ohcl, \n",
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" volume_panel = 2,\n",
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" figratio=(2,1),\n",
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" figscale=1, \n",
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" type='candle', \n",
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" addplot=apds,\n",
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" returnfig=True)"
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],
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"execution_count": null,
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"outputs": []
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}
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]
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}
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