{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Strategy Analysis with **Pandas TA** and AI/ML\n",
"* This is a **Work in Progress** and subject to change!\n",
"* Contributions are welcome and accepted!\n",
"* Examples below are for **educational purposes only**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Required Packages\n",
"##### Uncomment the packages you need to install or are missing"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#!pip install numpy\n",
"#!pip install pandas\n",
"#!pip install mplfinance\n",
"#!pip install pandas-datareader\n",
"#!pip install requests_cache\n",
"#!pip install alphaVantage-api"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"Numpy v1.18.3\n",
"Pandas v1.1.0\n",
"mplfinance v0.12.6a3\n",
"Pandas TA v0.1.95b\n",
"Pandas Data Reader v0.9.0\n",
"yfinance v0.1.54\n"
]
}
],
"source": [
"%pylab inline\n",
"import datetime as dt\n",
"import random as rnd\n",
"from sys import float_info as sflt\n",
"\n",
"import numpy as np # pip install numpy\n",
"import pandas as pd # pip install pandas\n",
"pd.set_option('max_rows', 100)\n",
"pd.set_option('max_columns', 20)\n",
"\n",
"import mplfinance as mpf # pip mplfinance\n",
"import pandas_datareader as pdr # pip install pandas-datareader\n",
"import requests_cache # pip install requests_cache\n",
"\n",
"from alphaVantageAPI.alphavantage import AlphaVantage # pip install alphaVantage-api\n",
"import pandas_ta as ta\n",
"\n",
"# from pandas_datareader.famafrench import get_available_datasets\n",
"import yfinance as yf\n",
"yf.pdr_override() # <== that's all it takes :-)\n",
"\n",
"# from watchlist import famafrench, Fred, Watchlist\n",
"from watchlist import Watchlist\n",
"# from watchlist import Fred, Watchlist\n",
"\n",
"print(f\"Numpy v{np.__version__}\")\n",
"print(f\"Pandas v{pd.__version__}\")\n",
"print(f\"mplfinance v{mpf.__version__}\")\n",
"print(f\"Pandas TA v{ta.version}\")\n",
"print(f\"Pandas Data Reader v{pdr.__version__}\")\n",
"print(f\"yfinance v{yf.__version__}\")\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MISC Functions"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def cscheme(colors: str):\n",
" aliases = {\n",
" \"BkBu\": [\"black\", \"blue\"],\n",
" \"gr\": [\"green\", \"red\"],\n",
" \"grays\": [\"silver\", \"gray\"],\n",
" \"mas\": [\"black\", \"green\", \"orange\", \"red\"],\n",
" \"bbands\": [\"blue\", \"navy\", \"blue\"],\n",
" \"kc\": [\"purple\", \"fuchsia\", \"purple\"],\n",
" }\n",
" aliases[\"default\"] = aliases[\"gr\"]\n",
" return aliases[colors]\n",
"\n",
"def recent_bars(df, tf: str = \"1y\"):\n",
" # All Data: 0, Last Four Years: 0.25, Last Two Years: 0.5, This Year: 1, Last Half Year: 2, Last Quarter: 3\n",
" yearly_divisor = {\"all\": 0, \"10y\": 0.1, \"5y\": 0.2, \"4y\": 0.25, \"3y\": 1./3, \"2y\": 0.5, \"1y\": 1, \"6mo\": 2, \"3mo\": 3}\n",
" yd = yearly_divisor[tf] if tf in yearly_divisor.keys() else 0\n",
" return int(ta.TRADING_DAYS_PER_YEAR / yd) if yd > 0 else df.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Collect some Data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[!] Loading All: SPY, QQQ\n",
"[i] Loaded['D']: SPY_D.csv\n",
"[+] Strategy: Common Price and Volume SMAs\n",
"[i] Indicator arguments: {'timed': True, 'append': True}\n",
"[i] Total indicators: 5\n",
"[i] Columns added: 5\n",
"[i] Runtime: 46.8974 ms (0.0469 s)\n",
"[+] Downloading['D']: QQQ\n",
"[+] Strategy: Common Price and Volume SMAs\n",
"[i] Indicator arguments: {'timed': True, 'append': True}\n",
"[i] Total indicators: 5\n",
"[i] Columns added: 5\n",
"[i] Runtime: 17.1018 ms (0.0171 s)\n"
]
}
],
"source": [
"tickers = [\"SPY\", \"QQQ\"]\n",
"watch = Watchlist(tickers)\n",
"watch.load(tickers, timed=True, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['open', 'high', 'low', 'close', 'volume', 'SMA_10', 'SMA_20', 'SMA_50',\n",
" 'SMA_200', 'VOL_SMA_20', '0'],\n",
" dtype='object')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ticker = tickers[1]\n",
"watch.data[ticker].ta.constants(True, [0, 0, 0])\n",
"watch.data[ticker].columns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"recent = recent_bars(watch.data[ticker], \"2y\")\n",
"asset = watch.data[ticker].copy().tail(recent)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build some Trends"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" CLTR | \n",
" TR_LOGRET | \n",
" CLTR_Trends | \n",
" CLTR_Trades | \n",
"
\n",
" \n",
" | date | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2020-08-17 | \n",
" 0.392134 | \n",
" 0.011544 | \n",
" 1 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2020-08-18 | \n",
" 0.401713 | \n",
" 0.009579 | \n",
" 1 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2020-08-19 | \n",
" 0.394963 | \n",
" -0.006750 | \n",
" 1 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2020-08-20 | \n",
" 0.408739 | \n",
" 0.013776 | \n",
" 1 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2020-08-21 | \n",
" 0.415646 | \n",
" 0.006906 | \n",
" 1 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"