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pandas-ta/examples/PandasTA_Strategy_Examples.ipynb
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Kevin Johnson 53404e57cc ENH logo MAINT gitignore + update example notebooks
2021-01-24 12:25:42 -08:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas TA ([pandas_ta](https://github.com/twopirllc/pandas-ta)) Strategies for Custom Technical Analysis\n",
"\n",
"## Topics\n",
"- What is a Pandas TA Strategy?\n",
" - Builtin Strategies: __AllStrategy__ and __CommonStrategy__\n",
" - Creating Strategies\n",
"- Watchlist Class\n",
" - Strategy Management and Execution\n",
" - **NOTE:** The **watchlist** module is independent of Pandas TA. To easily use it, copy it from your local pandas_ta installation directory into your project directory.\n",
"- Indicator Composition/Chaining for more Complex Strategies\n",
" - Comprehensive Example: _MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns_"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import datetime as dt\n",
"\n",
"import pandas as pd\n",
"import pandas_ta as ta\n",
"from alphaVantageAPI.alphavantage import AlphaVantage # pip install alphaVantage-api\n",
"\n",
"from watchlist import Watchlist # Is this failing? If so, copy it locally. See above.\n",
"%pylab inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# What is a Pandas TA Strategy?\n",
"A _Strategy_ is a simple way to name and group your favorite TA indicators. Technically, a _Strategy_ is a simple Data Class to contain list of indicators and their parameters. __Note__: _Strategy_ is experimental and subject to change. Pandas TA comes with two basic Strategies: __AllStrategy__ and __CommonStrategy__.\n",
"\n",
"## Strategy Requirements:\n",
"- _name_: Some short memorable string. _Note_: Case-insensitive \"All\" is reserved.\n",
"- _ta_: A list of dicts containing keyword arguments to identify the indicator and the indicator's arguments\n",
"\n",
"## Optional Requirements:\n",
"- _description_: A more detailed description of what the Strategy tries to capture. Default: None\n",
"- _created_: At datetime string of when it was created. Default: Automatically generated.\n",
"\n",
"### Things to note:\n",
"- A Strategy will __fail__ when consumed by Pandas TA if there is no {\"kind\": \"indicator name\"} attribute."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Builtin Examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### All"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name = All\n",
"description = All the indicators with their default settings. Pandas TA default.\n",
"created = 01/23/2021, 12:36:24\n",
"ta = None\n"
]
}
],
"source": [
"AllStrategy = ta.AllStrategy\n",
"print(\"name =\", AllStrategy.name)\n",
"print(\"description =\", AllStrategy.description)\n",
"print(\"created =\", AllStrategy.created)\n",
"print(\"ta =\", AllStrategy.ta)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Common"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name = Common Price and Volume SMAs\n",
"description = Common Price SMAs: 10, 20, 50, 200 and Volume SMA: 20.\n",
"created = 01/23/2021, 12:36:24\n",
"ta = [{'kind': 'sma', 'length': 10}, {'kind': 'sma', 'length': 20}, {'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}, {'kind': 'sma', 'close': 'volume', 'length': 20, 'prefix': 'VOL'}]\n"
]
}
],
"source": [
"CommonStrategy = ta.CommonStrategy\n",
"print(\"name =\", CommonStrategy.name)\n",
"print(\"description =\", CommonStrategy.description)\n",
"print(\"created =\", CommonStrategy.created)\n",
"print(\"ta =\", CommonStrategy.ta)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating Strategies"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simple Strategy A"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Strategy(name='A', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}], description='TA Description', created='01/23/2021, 12:36:24')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custom_a = ta.Strategy(name=\"A\", ta=[{\"kind\": \"sma\", \"length\": 50}, {\"kind\": \"sma\", \"length\": 200}])\n",
"custom_a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simple Strategy B"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Strategy(name='B', ta=[{'kind': 'ema', 'length': 8}, {'kind': 'ema', 'length': 21}, {'kind': 'log_return', 'cumulative': True}, {'kind': 'rsi'}, {'kind': 'supertrend'}], description='TA Description', created='01/23/2021, 12:36:24')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custom_b = ta.Strategy(name=\"B\", ta=[{\"kind\": \"ema\", \"length\": 8}, {\"kind\": \"ema\", \"length\": 21}, {\"kind\": \"log_return\", \"cumulative\": True}, {\"kind\": \"rsi\"}, {\"kind\": \"supertrend\"}])\n",
"custom_b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bad Strategy. (Misspelled Indicator)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Strategy(name='Runtime Failure', ta=[{'kind': 'percet_return'}], description='TA Description', created='01/23/2021, 12:36:24')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Misspelled indicator, will fail later when ran with Pandas TA\n",
"custom_run_failure = ta.Strategy(name=\"Runtime Failure\", ta=[{\"kind\": \"percet_return\"}])\n",
"custom_run_failure"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Strategy Management and Execution with _Watchlist_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialize AlphaVantage Data Source"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AlphaVantage(\n",
" end_point:str = https://www.alphavantage.co/query,\n",
" api_key:str = YOUR API KEY,\n",
" export:bool = True,\n",
" export_path:str = .,\n",
" output_size:str = full,\n",
" output:str = csv,\n",
" datatype:str = json,\n",
" clean:bool = True,\n",
" proxy:dict = {}\n",
")"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AV = AlphaVantage(\n",
" api_key=\"YOUR API KEY\", premium=False,\n",
" output_size='full', clean=True,\n",
" export_path=\".\", export=True\n",
")\n",
"AV"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Watchlist and set it's 'ds' to AlphaVantage"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"watch = Watchlist([\"SPY\", \"IWM\"], timed=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Info about the Watchlist. Note, the default Strategy is \"All\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Watch(name='Watch: SPY, IWM', tickers[2]='SPY, IWM', tf='D', strategy[5]='Common Price and Volume SMAs')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"watch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Help about Watchlist"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on class Watchlist in module watchlist:\n",
"\n",
"class Watchlist(builtins.object)\n",
" | Watchlist(tickers: list, tf: str = None, name: str = None, strategy: pandas_ta.core.Strategy = None, ds: object = None, **kwargs)\n",
" | \n",
" | # Watchlist Class (** This is subject to change! **)\n",
" | A simple Class to load/download financial market data and automatically\n",
" | apply Technical Analysis indicators with a Pandas TA Strategy.\n",
" | \n",
" | Default Strategy: pandas_ta.CommonStrategy\n",
" | \n",
" | ## Package Support:\n",
" | ### Data Source (Default: AlphaVantage)\n",
" | - AlphaVantage (pip install alphaVantage-api).\n",
" | - Python Binance (pip install python-binance). # Future Support\n",
" | - Yahoo Finance (pip install yfinance). # Almost Supported\n",
" | \n",
" | # Technical Analysis:\n",
" | - Pandas TA (pip install pandas_ta)\n",
" | \n",
" | ## Required Arguments:\n",
" | - tickers: A list of strings containing tickers. Example: [\"SPY\", \"AAPL\"]\n",
" | \n",
" | Methods defined here:\n",
" | \n",
" | __init__(self, tickers: list, tf: str = None, name: str = None, strategy: pandas_ta.core.Strategy = None, ds: object = None, **kwargs)\n",
" | Initialize self. See help(type(self)) for accurate signature.\n",
" | \n",
" | __repr__(self) -> str\n",
" | Return repr(self).\n",
" | \n",
" | indicators(self, *args, **kwargs) -> <built-in function any>\n",
" | Returns the list of indicators that are available with Pandas Ta.\n",
" | \n",
" | load(self, ticker: str = None, tf: str = None, index: str = 'date', drop: list = [], plot: bool = False, **kwargs) -> pandas.core.frame.DataFrame\n",
" | Loads or Downloads (if a local csv does not exist) the data from the\n",
" | Data Source. When successful, it returns a Data Frame for the requested\n",
" | ticker. If no tickers are given, it loads all the tickers.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors defined here:\n",
" | \n",
" | __dict__\n",
" | dictionary for instance variables (if defined)\n",
" | \n",
" | __weakref__\n",
" | list of weak references to the object (if defined)\n",
" | \n",
" | data\n",
" | When not None, it contains a dictionary of DataFrames keyed by ticker. data = {\"SPY\": pd.DataFrame, ...}\n",
" | \n",
" | name\n",
" | The name of the Watchlist. Default: \"Watchlist: {Watchlist.tickers}\".\n",
" | \n",
" | strategy\n",
" | Sets a valid Strategy. Default: pandas_ta.CommonStrategy\n",
" | \n",
" | tf\n",
" | Alias for timeframe. Default: 'D'\n",
" | \n",
" | tickers\n",
" | tickers\n",
" | \n",
" | If a string, it it converted to a list. Example: \"AAPL\" -> [\"AAPL\"]\n",
" | * Does not accept, comma seperated strings.\n",
" | If a list, checks if it is a list of strings.\n",
" | \n",
" | verbose\n",
" | Toggle the verbose property. Default: False\n",
"\n"
]
}
],
"source": [
"help(Watchlist)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Default Strategy is \"Common\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[!] Loading All: SPY, IWM\n",
"[i] Loaded['D']: SPY_D.csv\n",
"[+] Strategy: Common Price and Volume SMAs\n",
"[i] Indicator arguments: {'timed': False, 'append': True}\n",
"[i] Multiprocessing: 8 of 8 cores.\n",
"[i] Total indicators: 5\n",
"[i] Columns added: 5\n",
"[i] Loaded['D']: IWM_D.csv\n",
"[+] Strategy: Common Price and Volume SMAs\n",
"[i] Indicator arguments: {'timed': False, 'append': True}\n",
"[i] Multiprocessing: 8 of 8 cores.\n",
"[i] Total indicators: 5\n",
"[i] Columns added: 5\n"
]
}
],
"source": [
"# No arguments loads all the tickers and applies the Strategy to each ticker.\n",
"# The result can be accessed with Watchlist's 'data' property which returns a \n",
"# dictionary keyed by ticker and DataFrames as values \n",
"watch.load(verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'SPY': open high low close volume SMA_10 \\\n",
" date \n",
" 1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN \n",
" 1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN \n",
" 1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN \n",
" 1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN \n",
" 1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN \n",
" ... ... ... ... ... ... ... \n",
" 2020-09-28 333.2200 334.9600 332.1500 334.1900 64584614.0 331.181 \n",
" 2020-09-29 333.9700 334.7700 331.6209 332.3700 51531594.0 330.401 \n",
" 2020-09-30 333.0900 338.2900 332.8800 334.8900 104081136.0 330.008 \n",
" 2020-10-01 337.6900 338.7400 335.0100 337.0400 88698745.0 330.128 \n",
" 2020-10-02 331.7000 337.0126 331.1900 333.8400 89431112.0 330.447 \n",
" \n",
" SMA_20 SMA_50 SMA_200 VOL_SMA_20 \n",
" date \n",
" 1999-11-01 NaN NaN NaN NaN \n",
" 1999-11-02 NaN NaN NaN NaN \n",
" 1999-11-03 NaN NaN NaN NaN \n",
" 1999-11-04 NaN NaN NaN NaN \n",
" 1999-11-05 NaN NaN NaN NaN \n",
" ... ... ... ... ... \n",
" 2020-09-28 336.9395 334.8642 310.30500 86281647.00 \n",
" 2020-09-29 336.0925 335.0252 310.38120 85553267.55 \n",
" 2020-09-30 335.2070 335.2228 310.46905 88007358.10 \n",
" 2020-10-01 334.1740 335.4264 310.55675 88965293.60 \n",
" 2020-10-02 333.5965 335.6440 310.62810 86036292.75 \n",
" \n",
" [5265 rows x 10 columns],\n",
" 'IWM': open high low close volume SMA_10 SMA_20 \\\n",
" date \n",
" 2000-05-26 91.06 91.44 90.630 91.44 37400.0 NaN NaN \n",
" 2000-05-30 92.75 94.81 92.750 94.81 28800.0 NaN NaN \n",
" 2000-05-31 95.13 96.38 95.130 95.75 18000.0 NaN NaN \n",
" 2000-06-01 97.11 97.31 97.110 97.31 3500.0 NaN NaN \n",
" 2000-06-02 101.70 102.40 101.700 102.40 14700.0 NaN NaN \n",
" ... ... ... ... ... ... ... ... \n",
" 2020-09-28 148.37 150.44 146.404 150.02 17600904.0 149.672 151.4385 \n",
" 2020-09-29 149.85 150.28 148.000 149.34 18703037.0 149.269 151.1340 \n",
" 2020-09-30 149.90 151.97 148.490 149.79 29073746.0 148.766 150.7630 \n",
" 2020-10-01 150.81 152.20 149.490 152.18 25871962.0 148.615 150.4490 \n",
" 2020-10-02 149.41 153.58 148.990 152.85 29451992.0 148.571 150.4025 \n",
" \n",
" SMA_50 SMA_200 VOL_SMA_20 \n",
" date \n",
" 2000-05-26 NaN NaN NaN \n",
" 2000-05-30 NaN NaN NaN \n",
" 2000-05-31 NaN NaN NaN \n",
" 2000-06-01 NaN NaN NaN \n",
" 2000-06-02 NaN NaN NaN \n",
" ... ... ... ... \n",
" 2020-09-28 152.3430 144.94380 24197653.10 \n",
" 2020-09-29 152.4106 144.87070 24280229.40 \n",
" 2020-09-30 152.4458 144.80300 24951209.50 \n",
" 2020-10-01 152.5272 144.74450 25406635.15 \n",
" 2020-10-02 152.6190 144.68525 25273355.50 \n",
" \n",
" [5121 rows x 10 columns]}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"watch.data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" <th>SMA_10</th>\n",
" <th>SMA_20</th>\n",
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" <th>1999-11-01</th>\n",
" <td>136.5000</td>\n",
" <td>137.0000</td>\n",
" <td>135.5625</td>\n",
" <td>135.5625</td>\n",
" <td>4006500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-02</th>\n",
" <td>135.9687</td>\n",
" <td>137.2500</td>\n",
" <td>134.5937</td>\n",
" <td>134.5937</td>\n",
" <td>6516900.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>1999-11-03</th>\n",
" <td>136.0000</td>\n",
" <td>136.3750</td>\n",
" <td>135.1250</td>\n",
" <td>135.5000</td>\n",
" <td>7222300.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>1999-11-04</th>\n",
" <td>136.7500</td>\n",
" <td>137.3593</td>\n",
" <td>135.7656</td>\n",
" <td>136.5312</td>\n",
" <td>7907500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-05</th>\n",
" <td>138.6250</td>\n",
" <td>139.1093</td>\n",
" <td>136.7812</td>\n",
" <td>137.8750</td>\n",
" <td>7431500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2020-09-28</th>\n",
" <td>333.2200</td>\n",
" <td>334.9600</td>\n",
" <td>332.1500</td>\n",
" <td>334.1900</td>\n",
" <td>64584614.0</td>\n",
" <td>331.181</td>\n",
" <td>336.9395</td>\n",
" <td>334.8642</td>\n",
" <td>310.30500</td>\n",
" <td>86281647.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-29</th>\n",
" <td>333.9700</td>\n",
" <td>334.7700</td>\n",
" <td>331.6209</td>\n",
" <td>332.3700</td>\n",
" <td>51531594.0</td>\n",
" <td>330.401</td>\n",
" <td>336.0925</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2020-09-30</th>\n",
" <td>333.0900</td>\n",
" <td>338.2900</td>\n",
" <td>332.8800</td>\n",
" <td>334.8900</td>\n",
" <td>104081136.0</td>\n",
" <td>330.008</td>\n",
" <td>335.2070</td>\n",
" <td>335.2228</td>\n",
" <td>310.46905</td>\n",
" <td>88007358.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-01</th>\n",
" <td>337.6900</td>\n",
" <td>338.7400</td>\n",
" <td>335.0100</td>\n",
" <td>337.0400</td>\n",
" <td>88698745.0</td>\n",
" <td>330.128</td>\n",
" <td>334.1740</td>\n",
" <td>335.4264</td>\n",
" <td>310.55675</td>\n",
" <td>88965293.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-02</th>\n",
" <td>331.7000</td>\n",
" <td>337.0126</td>\n",
" <td>331.1900</td>\n",
" <td>333.8400</td>\n",
" <td>89431112.0</td>\n",
" <td>330.447</td>\n",
" <td>333.5965</td>\n",
" <td>335.6440</td>\n",
" <td>310.62810</td>\n",
" <td>86036292.75</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5265 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" open high low close volume SMA_10 \\\n",
"date \n",
"1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN \n",
"1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN \n",
"1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN \n",
"1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN \n",
"1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN \n",
"... ... ... ... ... ... ... \n",
"2020-09-28 333.2200 334.9600 332.1500 334.1900 64584614.0 331.181 \n",
"2020-09-29 333.9700 334.7700 331.6209 332.3700 51531594.0 330.401 \n",
"2020-09-30 333.0900 338.2900 332.8800 334.8900 104081136.0 330.008 \n",
"2020-10-01 337.6900 338.7400 335.0100 337.0400 88698745.0 330.128 \n",
"2020-10-02 331.7000 337.0126 331.1900 333.8400 89431112.0 330.447 \n",
"\n",
" SMA_20 SMA_50 SMA_200 VOL_SMA_20 \n",
"date \n",
"1999-11-01 NaN NaN NaN NaN \n",
"1999-11-02 NaN NaN NaN NaN \n",
"1999-11-03 NaN NaN NaN NaN \n",
"1999-11-04 NaN NaN NaN NaN \n",
"1999-11-05 NaN NaN NaN NaN \n",
"... ... ... ... ... \n",
"2020-09-28 336.9395 334.8642 310.30500 86281647.00 \n",
"2020-09-29 336.0925 335.0252 310.38120 85553267.55 \n",
"2020-09-30 335.2070 335.2228 310.46905 88007358.10 \n",
"2020-10-01 334.1740 335.4264 310.55675 88965293.60 \n",
"2020-10-02 333.5965 335.6440 310.62810 86036292.75 \n",
"\n",
"[5265 rows x 10 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"watch.data[\"SPY\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Easy to swap Strategies and run them"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Running Simple Strategy A"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Strategy(name='A', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}], description='TA Description', created='01/23/2021, 12:36:24')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load custom_a into Watchlist and verify\n",
"watch.strategy = custom_a\n",
"# watch.debug = True\n",
"watch.strategy"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[i] Loaded['D']: IWM_D.csv\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" <th>SMA_50</th>\n",
" <th>SMA_200</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-05-26</th>\n",
" <td>91.06</td>\n",
" <td>91.44</td>\n",
" <td>90.630</td>\n",
" <td>91.44</td>\n",
" <td>37400.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-05-30</th>\n",
" <td>92.75</td>\n",
" <td>94.81</td>\n",
" <td>92.750</td>\n",
" <td>94.81</td>\n",
" <td>28800.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-05-31</th>\n",
" <td>95.13</td>\n",
" <td>96.38</td>\n",
" <td>95.130</td>\n",
" <td>95.75</td>\n",
" <td>18000.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-06-01</th>\n",
" <td>97.11</td>\n",
" <td>97.31</td>\n",
" <td>97.110</td>\n",
" <td>97.31</td>\n",
" <td>3500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-06-02</th>\n",
" <td>101.70</td>\n",
" <td>102.40</td>\n",
" <td>101.700</td>\n",
" <td>102.40</td>\n",
" <td>14700.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-28</th>\n",
" <td>148.37</td>\n",
" <td>150.44</td>\n",
" <td>146.404</td>\n",
" <td>150.02</td>\n",
" <td>17600904.0</td>\n",
" <td>152.3430</td>\n",
" <td>144.94380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-29</th>\n",
" <td>149.85</td>\n",
" <td>150.28</td>\n",
" <td>148.000</td>\n",
" <td>149.34</td>\n",
" <td>18703037.0</td>\n",
" <td>152.4106</td>\n",
" <td>144.87070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-30</th>\n",
" <td>149.90</td>\n",
" <td>151.97</td>\n",
" <td>148.490</td>\n",
" <td>149.79</td>\n",
" <td>29073746.0</td>\n",
" <td>152.4458</td>\n",
" <td>144.80300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-01</th>\n",
" <td>150.81</td>\n",
" <td>152.20</td>\n",
" <td>149.490</td>\n",
" <td>152.18</td>\n",
" <td>25871962.0</td>\n",
" <td>152.5272</td>\n",
" <td>144.74450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-02</th>\n",
" <td>149.41</td>\n",
" <td>153.58</td>\n",
" <td>148.990</td>\n",
" <td>152.85</td>\n",
" <td>29451992.0</td>\n",
" <td>152.6190</td>\n",
" <td>144.68525</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5121 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" open high low close volume SMA_50 SMA_200\n",
"date \n",
"2000-05-26 91.06 91.44 90.630 91.44 37400.0 NaN NaN\n",
"2000-05-30 92.75 94.81 92.750 94.81 28800.0 NaN NaN\n",
"2000-05-31 95.13 96.38 95.130 95.75 18000.0 NaN NaN\n",
"2000-06-01 97.11 97.31 97.110 97.31 3500.0 NaN NaN\n",
"2000-06-02 101.70 102.40 101.700 102.40 14700.0 NaN NaN\n",
"... ... ... ... ... ... ... ...\n",
"2020-09-28 148.37 150.44 146.404 150.02 17600904.0 152.3430 144.94380\n",
"2020-09-29 149.85 150.28 148.000 149.34 18703037.0 152.4106 144.87070\n",
"2020-09-30 149.90 151.97 148.490 149.79 29073746.0 152.4458 144.80300\n",
"2020-10-01 150.81 152.20 149.490 152.18 25871962.0 152.5272 144.74450\n",
"2020-10-02 149.41 153.58 148.990 152.85 29451992.0 152.6190 144.68525\n",
"\n",
"[5121 rows x 7 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"watch.load(\"IWM\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Running Simple Strategy B"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Strategy(name='B', ta=[{'kind': 'ema', 'length': 8}, {'kind': 'ema', 'length': 21}, {'kind': 'log_return', 'cumulative': True}, {'kind': 'rsi'}, {'kind': 'supertrend'}], description='TA Description', created='01/23/2021, 12:36:24')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load custom_b into Watchlist and verify\n",
"watch.strategy = custom_b\n",
"watch.strategy"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[i] Loaded['D']: SPY_D.csv\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
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"\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" <th>EMA_8</th>\n",
" <th>EMA_21</th>\n",
" <th>CUMLOGRET_1</th>\n",
" <th>RSI_14</th>\n",
" <th>SUPERT_7_3.0</th>\n",
" <th>SUPERTd_7_3.0</th>\n",
" <th>SUPERTl_7_3.0</th>\n",
" <th>SUPERTs_7_3.0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1999-11-01</th>\n",
" <td>136.5000</td>\n",
" <td>137.0000</td>\n",
" <td>135.5625</td>\n",
" <td>135.5625</td>\n",
" <td>4006500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-02</th>\n",
" <td>135.9687</td>\n",
" <td>137.2500</td>\n",
" <td>134.5937</td>\n",
" <td>134.5937</td>\n",
" <td>6516900.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.007172</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-03</th>\n",
" <td>136.0000</td>\n",
" <td>136.3750</td>\n",
" <td>135.1250</td>\n",
" <td>135.5000</td>\n",
" <td>7222300.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.000461</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-04</th>\n",
" <td>136.7500</td>\n",
" <td>137.3593</td>\n",
" <td>135.7656</td>\n",
" <td>136.5312</td>\n",
" <td>7907500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.007120</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-05</th>\n",
" <td>138.6250</td>\n",
" <td>139.1093</td>\n",
" <td>136.7812</td>\n",
" <td>137.8750</td>\n",
" <td>7431500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.016915</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-28</th>\n",
" <td>333.2200</td>\n",
" <td>334.9600</td>\n",
" <td>332.1500</td>\n",
" <td>334.1900</td>\n",
" <td>64584614.0</td>\n",
" <td>330.408828</td>\n",
" <td>334.010756</td>\n",
" <td>0.902277</td>\n",
" <td>49.833349</td>\n",
" <td>344.119874</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>344.119874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-29</th>\n",
" <td>333.9700</td>\n",
" <td>334.7700</td>\n",
" <td>331.6209</td>\n",
" <td>332.3700</td>\n",
" <td>51531594.0</td>\n",
" <td>330.844644</td>\n",
" <td>333.861596</td>\n",
" <td>0.896816</td>\n",
" <td>48.051629</td>\n",
" <td>344.119874</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>344.119874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-30</th>\n",
" <td>333.0900</td>\n",
" <td>338.2900</td>\n",
" <td>332.8800</td>\n",
" <td>334.8900</td>\n",
" <td>104081136.0</td>\n",
" <td>331.743612</td>\n",
" <td>333.955087</td>\n",
" <td>0.904369</td>\n",
" <td>50.680975</td>\n",
" <td>344.119874</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>344.119874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-01</th>\n",
" <td>337.6900</td>\n",
" <td>338.7400</td>\n",
" <td>335.0100</td>\n",
" <td>337.0400</td>\n",
" <td>88698745.0</td>\n",
" <td>332.920587</td>\n",
" <td>334.235534</td>\n",
" <td>0.910769</td>\n",
" <td>52.872627</td>\n",
" <td>344.119874</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>344.119874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-02</th>\n",
" <td>331.7000</td>\n",
" <td>337.0126</td>\n",
" <td>331.1900</td>\n",
" <td>333.8400</td>\n",
" <td>89431112.0</td>\n",
" <td>333.124901</td>\n",
" <td>334.199576</td>\n",
" <td>0.901229</td>\n",
" <td>49.357005</td>\n",
" <td>344.119874</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>344.119874</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5265 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" open high low close volume EMA_8 \\\n",
"date \n",
"1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN \n",
"1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN \n",
"1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN \n",
"1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN \n",
"1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN \n",
"... ... ... ... ... ... ... \n",
"2020-09-28 333.2200 334.9600 332.1500 334.1900 64584614.0 330.408828 \n",
"2020-09-29 333.9700 334.7700 331.6209 332.3700 51531594.0 330.844644 \n",
"2020-09-30 333.0900 338.2900 332.8800 334.8900 104081136.0 331.743612 \n",
"2020-10-01 337.6900 338.7400 335.0100 337.0400 88698745.0 332.920587 \n",
"2020-10-02 331.7000 337.0126 331.1900 333.8400 89431112.0 333.124901 \n",
"\n",
" EMA_21 CUMLOGRET_1 RSI_14 SUPERT_7_3.0 SUPERTd_7_3.0 \\\n",
"date \n",
"1999-11-01 NaN NaN NaN 0.000000 1 \n",
"1999-11-02 NaN -0.007172 NaN NaN 1 \n",
"1999-11-03 NaN -0.000461 NaN NaN 1 \n",
"1999-11-04 NaN 0.007120 NaN NaN 1 \n",
"1999-11-05 NaN 0.016915 NaN NaN 1 \n",
"... ... ... ... ... ... \n",
"2020-09-28 334.010756 0.902277 49.833349 344.119874 -1 \n",
"2020-09-29 333.861596 0.896816 48.051629 344.119874 -1 \n",
"2020-09-30 333.955087 0.904369 50.680975 344.119874 -1 \n",
"2020-10-01 334.235534 0.910769 52.872627 344.119874 -1 \n",
"2020-10-02 334.199576 0.901229 49.357005 344.119874 -1 \n",
"\n",
" SUPERTl_7_3.0 SUPERTs_7_3.0 \n",
"date \n",
"1999-11-01 NaN NaN \n",
"1999-11-02 NaN NaN \n",
"1999-11-03 NaN NaN \n",
"1999-11-04 NaN NaN \n",
"1999-11-05 NaN NaN \n",
"... ... ... \n",
"2020-09-28 NaN 344.119874 \n",
"2020-09-29 NaN 344.119874 \n",
"2020-09-30 NaN 344.119874 \n",
"2020-10-01 NaN 344.119874 \n",
"2020-10-02 NaN 344.119874 \n",
"\n",
"[5265 rows x 13 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"watch.load(\"SPY\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Running Bad Strategy. (Misspelled indicator)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Strategy(name='Runtime Failure', ta=[{'kind': 'percet_return'}], description='TA Description', created='01/23/2021, 12:36:24')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load custom_run_failure into Watchlist and verify\n",
"watch.strategy = custom_run_failure\n",
"watch.strategy"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[i] Loaded['D']: IWM_D.csv\n",
"[X] Oops! 'AnalysisIndicators' object has no attribute 'percet_return'\n"
]
}
],
"source": [
"try:\n",
" iwm = watch.load(\"IWM\")\n",
"except AttributeError as error:\n",
" print(f\"[X] Oops! {error}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Indicator Composition/Chaining\n",
"- When you need an indicator to depend on the value of a prior indicator\n",
"- Utilitze _prefix_ or _suffix_ to help identify unique columns or avoid column name clashes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Volume MAs and MA chains"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Strategy(name='Volume MAs and Price MA chain', ta=[{'kind': 'ema', 'close': 'volume', 'length': 10, 'prefix': 'VOLUME'}, {'kind': 'sma', 'close': 'volume', 'length': 20, 'prefix': 'VOLUME'}, {'kind': 'ema', 'length': 5}, {'kind': 'linreg', 'close': 'EMA_5', 'length': 8, 'prefix': 'EMA_5'}], description='TA Description', created='01/23/2021, 12:36:24')"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Set EMA's and SMA's 'close' to 'volume' to create Volume MAs, prefix 'volume' MAs with 'VOLUME' so easy to identify the column\n",
"# Take a price EMA and apply LINREG from EMA's output\n",
"volmas_price_ma_chain = [\n",
" {\"kind\":\"ema\", \"close\": \"volume\", \"length\": 10, \"prefix\": \"VOLUME\"},\n",
" {\"kind\":\"sma\", \"close\": \"volume\", \"length\": 20, \"prefix\": \"VOLUME\"},\n",
" {\"kind\":\"ema\", \"length\": 5},\n",
" {\"kind\":\"linreg\", \"close\": \"EMA_5\", \"length\": 8, \"prefix\": \"EMA_5\"},\n",
"]\n",
"vp_ma_chain_ta = ta.Strategy(\"Volume MAs and Price MA chain\", volmas_price_ma_chain)\n",
"vp_ma_chain_ta"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Volume MAs and Price MA chain'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Update the Watchlist\n",
"watch.strategy = vp_ma_chain_ta\n",
"watch.strategy.name"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[i] Loaded['D']: SPY_D.csv\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" <th>VOLUME_EMA_10</th>\n",
" <th>VOLUME_SMA_20</th>\n",
" <th>EMA_5</th>\n",
" <th>EMA_5_LR_8</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1999-11-01</th>\n",
" <td>136.5000</td>\n",
" <td>137.0000</td>\n",
" <td>135.5625</td>\n",
" <td>135.5625</td>\n",
" <td>4006500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-02</th>\n",
" <td>135.9687</td>\n",
" <td>137.2500</td>\n",
" <td>134.5937</td>\n",
" <td>134.5937</td>\n",
" <td>6516900.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-03</th>\n",
" <td>136.0000</td>\n",
" <td>136.3750</td>\n",
" <td>135.1250</td>\n",
" <td>135.5000</td>\n",
" <td>7222300.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-04</th>\n",
" <td>136.7500</td>\n",
" <td>137.3593</td>\n",
" <td>135.7656</td>\n",
" <td>136.5312</td>\n",
" <td>7907500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-05</th>\n",
" <td>138.6250</td>\n",
" <td>139.1093</td>\n",
" <td>136.7812</td>\n",
" <td>137.8750</td>\n",
" <td>7431500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>136.012480</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-28</th>\n",
" <td>333.2200</td>\n",
" <td>334.9600</td>\n",
" <td>332.1500</td>\n",
" <td>334.1900</td>\n",
" <td>64584614.0</td>\n",
" <td>7.861837e+07</td>\n",
" <td>86281647.00</td>\n",
" <td>329.781966</td>\n",
" <td>327.921896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-29</th>\n",
" <td>333.9700</td>\n",
" <td>334.7700</td>\n",
" <td>331.6209</td>\n",
" <td>332.3700</td>\n",
" <td>51531594.0</td>\n",
" <td>7.369350e+07</td>\n",
" <td>85553267.55</td>\n",
" <td>330.644644</td>\n",
" <td>328.610232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-30</th>\n",
" <td>333.0900</td>\n",
" <td>338.2900</td>\n",
" <td>332.8800</td>\n",
" <td>334.8900</td>\n",
" <td>104081136.0</td>\n",
" <td>7.921853e+07</td>\n",
" <td>88007358.10</td>\n",
" <td>332.059763</td>\n",
" <td>329.854718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-01</th>\n",
" <td>337.6900</td>\n",
" <td>338.7400</td>\n",
" <td>335.0100</td>\n",
" <td>337.0400</td>\n",
" <td>88698745.0</td>\n",
" <td>8.094220e+07</td>\n",
" <td>88965293.60</td>\n",
" <td>333.719842</td>\n",
" <td>331.449495</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-02</th>\n",
" <td>331.7000</td>\n",
" <td>337.0126</td>\n",
" <td>331.1900</td>\n",
" <td>333.8400</td>\n",
" <td>89431112.0</td>\n",
" <td>8.248564e+07</td>\n",
" <td>86036292.75</td>\n",
" <td>333.759895</td>\n",
" <td>332.881012</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5265 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" open high low close volume \\\n",
"date \n",
"1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 \n",
"1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 \n",
"1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 \n",
"1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 \n",
"1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 \n",
"... ... ... ... ... ... \n",
"2020-09-28 333.2200 334.9600 332.1500 334.1900 64584614.0 \n",
"2020-09-29 333.9700 334.7700 331.6209 332.3700 51531594.0 \n",
"2020-09-30 333.0900 338.2900 332.8800 334.8900 104081136.0 \n",
"2020-10-01 337.6900 338.7400 335.0100 337.0400 88698745.0 \n",
"2020-10-02 331.7000 337.0126 331.1900 333.8400 89431112.0 \n",
"\n",
" VOLUME_EMA_10 VOLUME_SMA_20 EMA_5 EMA_5_LR_8 \n",
"date \n",
"1999-11-01 NaN NaN NaN NaN \n",
"1999-11-02 NaN NaN NaN NaN \n",
"1999-11-03 NaN NaN NaN NaN \n",
"1999-11-04 NaN NaN NaN NaN \n",
"1999-11-05 NaN NaN 136.012480 NaN \n",
"... ... ... ... ... \n",
"2020-09-28 7.861837e+07 86281647.00 329.781966 327.921896 \n",
"2020-09-29 7.369350e+07 85553267.55 330.644644 328.610232 \n",
"2020-09-30 7.921853e+07 88007358.10 332.059763 329.854718 \n",
"2020-10-01 8.094220e+07 88965293.60 333.719842 331.449495 \n",
"2020-10-02 8.248564e+07 86036292.75 333.759895 332.881012 \n",
"\n",
"[5265 rows x 9 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spy = watch.load(\"SPY\")\n",
"spy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### MACD BBANDS"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Strategy(name='MACD BBands', ta=[{'kind': 'macd'}, {'kind': 'bbands', 'close': 'MACD_12_26_9', 'length': 20, 'prefix': 'MACD'}], description='BBANDS_20 applied to MACD', created='01/23/2021, 12:36:24')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# MACD is the initial indicator that BBANDS depends on.\n",
"# Set BBANDS's 'close' to MACD's main signal, in this case 'MACD_12_26_9' and add a prefix (or suffix) so it's easier to identify\n",
"macd_bands_ta = [\n",
" {\"kind\":\"macd\"},\n",
" {\"kind\":\"bbands\", \"close\": \"MACD_12_26_9\", \"length\": 20, \"prefix\": \"MACD\"}\n",
"]\n",
"macd_bands_ta = ta.Strategy(\"MACD BBands\", macd_bands_ta, f\"BBANDS_{macd_bands_ta[1]['length']} applied to MACD\")\n",
"macd_bands_ta"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'MACD BBands'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Update the Watchlist\n",
"watch.strategy = macd_bands_ta\n",
"watch.strategy.name"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[i] Loaded['D']: SPY_D.csv\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" <th>MACD_12_26_9</th>\n",
" <th>MACDh_12_26_9</th>\n",
" <th>MACDs_12_26_9</th>\n",
" <th>MACD_BBL_20_2.0</th>\n",
" <th>MACD_BBM_20_2.0</th>\n",
" <th>MACD_BBU_20_2.0</th>\n",
" <th>MACD_BBB_20_2.0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1999-11-01</th>\n",
" <td>136.5000</td>\n",
" <td>137.0000</td>\n",
" <td>135.5625</td>\n",
" <td>135.5625</td>\n",
" <td>4006500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-02</th>\n",
" <td>135.9687</td>\n",
" <td>137.2500</td>\n",
" <td>134.5937</td>\n",
" <td>134.5937</td>\n",
" <td>6516900.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-03</th>\n",
" <td>136.0000</td>\n",
" <td>136.3750</td>\n",
" <td>135.1250</td>\n",
" <td>135.5000</td>\n",
" <td>7222300.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-04</th>\n",
" <td>136.7500</td>\n",
" <td>137.3593</td>\n",
" <td>135.7656</td>\n",
" <td>136.5312</td>\n",
" <td>7907500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-05</th>\n",
" <td>138.6250</td>\n",
" <td>139.1093</td>\n",
" <td>136.7812</td>\n",
" <td>137.8750</td>\n",
" <td>7431500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-28</th>\n",
" <td>333.2200</td>\n",
" <td>334.9600</td>\n",
" <td>332.1500</td>\n",
" <td>334.1900</td>\n",
" <td>64584614.0</td>\n",
" <td>-2.475445</td>\n",
" <td>-1.262367</td>\n",
" <td>-1.213078</td>\n",
" <td>-5.034888</td>\n",
" <td>1.888513</td>\n",
" <td>8.811913</td>\n",
" <td>733.211957</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-29</th>\n",
" <td>333.9700</td>\n",
" <td>334.7700</td>\n",
" <td>331.6209</td>\n",
" <td>332.3700</td>\n",
" <td>51531594.0</td>\n",
" <td>-2.252083</td>\n",
" <td>-0.831203</td>\n",
" <td>-1.420879</td>\n",
" <td>-5.348731</td>\n",
" <td>1.450066</td>\n",
" <td>8.248862</td>\n",
" <td>937.722469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-30</th>\n",
" <td>333.0900</td>\n",
" <td>338.2900</td>\n",
" <td>332.8800</td>\n",
" <td>334.8900</td>\n",
" <td>104081136.0</td>\n",
" <td>-1.850393</td>\n",
" <td>-0.343611</td>\n",
" <td>-1.506782</td>\n",
" <td>-5.445367</td>\n",
" <td>1.019390</td>\n",
" <td>7.484147</td>\n",
" <td>1268.357614</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-01</th>\n",
" <td>337.6900</td>\n",
" <td>338.7400</td>\n",
" <td>335.0100</td>\n",
" <td>337.0400</td>\n",
" <td>88698745.0</td>\n",
" <td>-1.343082</td>\n",
" <td>0.130960</td>\n",
" <td>-1.474042</td>\n",
" <td>-5.235913</td>\n",
" <td>0.587945</td>\n",
" <td>6.411803</td>\n",
" <td>1981.090096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-02</th>\n",
" <td>331.7000</td>\n",
" <td>337.0126</td>\n",
" <td>331.1900</td>\n",
" <td>333.8400</td>\n",
" <td>89431112.0</td>\n",
" <td>-1.185581</td>\n",
" <td>0.230769</td>\n",
" <td>-1.416350</td>\n",
" <td>-4.926333</td>\n",
" <td>0.197149</td>\n",
" <td>5.320631</td>\n",
" <td>5197.569576</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5265 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" open high low close volume MACD_12_26_9 \\\n",
"date \n",
"1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN \n",
"1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN \n",
"1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN \n",
"1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN \n",
"1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN \n",
"... ... ... ... ... ... ... \n",
"2020-09-28 333.2200 334.9600 332.1500 334.1900 64584614.0 -2.475445 \n",
"2020-09-29 333.9700 334.7700 331.6209 332.3700 51531594.0 -2.252083 \n",
"2020-09-30 333.0900 338.2900 332.8800 334.8900 104081136.0 -1.850393 \n",
"2020-10-01 337.6900 338.7400 335.0100 337.0400 88698745.0 -1.343082 \n",
"2020-10-02 331.7000 337.0126 331.1900 333.8400 89431112.0 -1.185581 \n",
"\n",
" MACDh_12_26_9 MACDs_12_26_9 MACD_BBL_20_2.0 MACD_BBM_20_2.0 \\\n",
"date \n",
"1999-11-01 NaN NaN NaN NaN \n",
"1999-11-02 NaN NaN NaN NaN \n",
"1999-11-03 NaN NaN NaN NaN \n",
"1999-11-04 NaN NaN NaN NaN \n",
"1999-11-05 NaN NaN NaN NaN \n",
"... ... ... ... ... \n",
"2020-09-28 -1.262367 -1.213078 -5.034888 1.888513 \n",
"2020-09-29 -0.831203 -1.420879 -5.348731 1.450066 \n",
"2020-09-30 -0.343611 -1.506782 -5.445367 1.019390 \n",
"2020-10-01 0.130960 -1.474042 -5.235913 0.587945 \n",
"2020-10-02 0.230769 -1.416350 -4.926333 0.197149 \n",
"\n",
" MACD_BBU_20_2.0 MACD_BBB_20_2.0 \n",
"date \n",
"1999-11-01 NaN NaN \n",
"1999-11-02 NaN NaN \n",
"1999-11-03 NaN NaN \n",
"1999-11-04 NaN NaN \n",
"1999-11-05 NaN NaN \n",
"... ... ... \n",
"2020-09-28 8.811913 733.211957 \n",
"2020-09-29 8.248862 937.722469 \n",
"2020-09-30 7.484147 1268.357614 \n",
"2020-10-01 6.411803 1981.090096 \n",
"2020-10-02 5.320631 5197.569576 \n",
"\n",
"[5265 rows x 12 columns]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spy = watch.load(\"SPY\")\n",
"spy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comprehensive Strategy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### MACD and RSI Momentum with BBANDS and SMAs and Cumulative Log Returns"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Strategy(name='Momo, Bands and SMAs and Cumulative Log Returns', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}, {'kind': 'bbands', 'length': 20}, {'kind': 'macd'}, {'kind': 'rsi'}, {'kind': 'log_return', 'cumulative': True}, {'kind': 'sma', 'close': 'CUMLOGRET_1', 'length': 5, 'suffix': 'CUMLOGRET'}], description='MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns', created='01/23/2021, 12:36:24')"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"momo_bands_sma_ta = [\n",
" {\"kind\":\"sma\", \"length\": 50},\n",
" {\"kind\":\"sma\", \"length\": 200},\n",
" {\"kind\":\"bbands\", \"length\": 20},\n",
" {\"kind\":\"macd\"},\n",
" {\"kind\":\"rsi\"},\n",
" {\"kind\":\"log_return\", \"cumulative\": True},\n",
" {\"kind\":\"sma\", \"close\": \"CUMLOGRET_1\", \"length\": 5, \"suffix\": \"CUMLOGRET\"},\n",
"]\n",
"momo_bands_sma_strategy = ta.Strategy(\n",
" \"Momo, Bands and SMAs and Cumulative Log Returns\", # name\n",
" momo_bands_sma_ta, # ta\n",
" \"MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns\" # description\n",
")\n",
"momo_bands_sma_strategy"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Momo, Bands and SMAs and Cumulative Log Returns'"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Update the Watchlist\n",
"watch.strategy = momo_bands_sma_strategy\n",
"watch.strategy.name"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[i] Loaded['D']: SPY_D.csv\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" <th>SMA_50</th>\n",
" <th>SMA_200</th>\n",
" <th>BBL_20_2.0</th>\n",
" <th>BBM_20_2.0</th>\n",
" <th>BBU_20_2.0</th>\n",
" <th>BBB_20_2.0</th>\n",
" <th>MACD_12_26_9</th>\n",
" <th>MACDh_12_26_9</th>\n",
" <th>MACDs_12_26_9</th>\n",
" <th>RSI_14</th>\n",
" <th>CUMLOGRET_1</th>\n",
" <th>SMA_5_CUMLOGRET</th>\n",
" <th>0</th>\n",
" <th>30</th>\n",
" <th>70</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1999-11-01</th>\n",
" <td>136.5000</td>\n",
" <td>137.0000</td>\n",
" <td>135.5625</td>\n",
" <td>135.5625</td>\n",
" <td>4006500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-02</th>\n",
" <td>135.9687</td>\n",
" <td>137.2500</td>\n",
" <td>134.5937</td>\n",
" <td>134.5937</td>\n",
" <td>6516900.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.007172</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-03</th>\n",
" <td>136.0000</td>\n",
" <td>136.3750</td>\n",
" <td>135.1250</td>\n",
" <td>135.5000</td>\n",
" <td>7222300.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.000461</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-04</th>\n",
" <td>136.7500</td>\n",
" <td>137.3593</td>\n",
" <td>135.7656</td>\n",
" <td>136.5312</td>\n",
" <td>7907500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.007120</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-11-05</th>\n",
" <td>138.6250</td>\n",
" <td>139.1093</td>\n",
" <td>136.7812</td>\n",
" <td>137.8750</td>\n",
" <td>7431500.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.016915</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>70</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close volume SMA_50 \\\n",
"date \n",
"1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN \n",
"1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN \n",
"1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN \n",
"1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN \n",
"1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN \n",
"\n",
" SMA_200 BBL_20_2.0 BBM_20_2.0 BBU_20_2.0 BBB_20_2.0 \\\n",
"date \n",
"1999-11-01 NaN NaN NaN NaN NaN \n",
"1999-11-02 NaN NaN NaN NaN NaN \n",
"1999-11-03 NaN NaN NaN NaN NaN \n",
"1999-11-04 NaN NaN NaN NaN NaN \n",
"1999-11-05 NaN NaN NaN NaN NaN \n",
"\n",
" MACD_12_26_9 MACDh_12_26_9 MACDs_12_26_9 RSI_14 CUMLOGRET_1 \\\n",
"date \n",
"1999-11-01 NaN NaN NaN NaN NaN \n",
"1999-11-02 NaN NaN NaN NaN -0.007172 \n",
"1999-11-03 NaN NaN NaN NaN -0.000461 \n",
"1999-11-04 NaN NaN NaN NaN 0.007120 \n",
"1999-11-05 NaN NaN NaN NaN 0.016915 \n",
"\n",
" SMA_5_CUMLOGRET 0 30 70 \n",
"date \n",
"1999-11-01 NaN 0 30 70 \n",
"1999-11-02 NaN 0 30 70 \n",
"1999-11-03 NaN 0 30 70 \n",
"1999-11-04 NaN 0 30 70 \n",
"1999-11-05 NaN 0 30 70 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spy = watch.load(\"SPY\")\n",
"# Apply constants to the DataFrame for indicators\n",
"spy.ta.constants(True, [0, 30, 70])\n",
"spy.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Additional Strategy Options"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ```params``` keyword takes a _tuple_ as a shorthand to the parameter arguments in order.\n",
"* **Note**: If the indicator arguments change, so will results. Breaking Changes will **always** be posted on the README.\n",
"\n",
"The ```col_numbers``` keyword takes a _tuple_ specifying which column to return if the result is a DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Strategy(name='EMA, MACD History, Outter BBands, Log Returns', ta=[{'kind': 'ema', 'params': (10,)}, {'kind': 'macd', 'params': (9, 19, 10), 'col_numbers': (1,)}, {'kind': 'bbands', 'col_numbers': (0, 2), 'col_names': ('LB', 'UB')}, {'kind': 'log_return', 'params': (5, False)}], description='EMA, MACD History, BBands(LB, UB), and Log Returns Strategy', created='01/23/2021, 12:36:24')"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"params_ta = [\n",
" {\"kind\":\"ema\", \"params\": (10,)},\n",
" # params sets MACD's keyword arguments: fast=9, slow=19, signal=10\n",
" # and returning the 2nd column: histogram\n",
" {\"kind\":\"macd\", \"params\": (9, 19, 10), \"col_numbers\": (1,)},\n",
" # Selects the Lower and Upper Bands and renames them LB and UB, ignoring the MB\n",
" {\"kind\":\"bbands\", \"col_numbers\": (0,2), \"col_names\": (\"LB\", \"UB\")},\n",
" {\"kind\":\"log_return\", \"params\": (5, False)},\n",
"]\n",
"params_ta_strategy = ta.Strategy(\n",
" \"EMA, MACD History, Outter BBands, Log Returns\", # name\n",
" params_ta, # ta\n",
" \"EMA, MACD History, BBands(LB, UB), and Log Returns Strategy\" # description\n",
")\n",
"params_ta_strategy"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'EMA, MACD History, Outter BBands, Log Returns'"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Update the Watchlist\n",
"watch.strategy = params_ta_strategy\n",
"watch.strategy.name"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[i] Loaded['D']: SPY_D.csv\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" <th>EMA_10</th>\n",
" <th>MACDh_9_19_10</th>\n",
" <th>LB</th>\n",
" <th>UB</th>\n",
" <th>LOGRET_5</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-09-28</th>\n",
" <td>333.22</td>\n",
" <td>334.9600</td>\n",
" <td>332.1500</td>\n",
" <td>334.19</td>\n",
" <td>64584614.0</td>\n",
" <td>331.135274</td>\n",
" <td>-0.761769</td>\n",
" <td>318.226448</td>\n",
" <td>337.517552</td>\n",
" <td>0.021841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-29</th>\n",
" <td>333.97</td>\n",
" <td>334.7700</td>\n",
" <td>331.6209</td>\n",
" <td>332.37</td>\n",
" <td>51531594.0</td>\n",
" <td>331.359770</td>\n",
" <td>-0.249828</td>\n",
" <td>317.965316</td>\n",
" <td>338.606684</td>\n",
" <td>0.006247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-30</th>\n",
" <td>333.09</td>\n",
" <td>338.2900</td>\n",
" <td>332.8800</td>\n",
" <td>334.89</td>\n",
" <td>104081136.0</td>\n",
" <td>332.001630</td>\n",
" <td>0.311580</td>\n",
" <td>321.342411</td>\n",
" <td>340.129589</td>\n",
" <td>0.037265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-01</th>\n",
" <td>337.69</td>\n",
" <td>338.7400</td>\n",
" <td>335.0100</td>\n",
" <td>337.04</td>\n",
" <td>88698745.0</td>\n",
" <td>332.917697</td>\n",
" <td>0.832955</td>\n",
" <td>327.202692</td>\n",
" <td>339.685308</td>\n",
" <td>0.041003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-02</th>\n",
" <td>331.70</td>\n",
" <td>337.0126</td>\n",
" <td>331.1900</td>\n",
" <td>333.84</td>\n",
" <td>89431112.0</td>\n",
" <td>333.085389</td>\n",
" <td>0.854917</td>\n",
" <td>331.050371</td>\n",
" <td>337.881629</td>\n",
" <td>0.015425</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close volume EMA_10 \\\n",
"date \n",
"2020-09-28 333.22 334.9600 332.1500 334.19 64584614.0 331.135274 \n",
"2020-09-29 333.97 334.7700 331.6209 332.37 51531594.0 331.359770 \n",
"2020-09-30 333.09 338.2900 332.8800 334.89 104081136.0 332.001630 \n",
"2020-10-01 337.69 338.7400 335.0100 337.04 88698745.0 332.917697 \n",
"2020-10-02 331.70 337.0126 331.1900 333.84 89431112.0 333.085389 \n",
"\n",
" MACDh_9_19_10 LB UB LOGRET_5 \n",
"date \n",
"2020-09-28 -0.761769 318.226448 337.517552 0.021841 \n",
"2020-09-29 -0.249828 317.965316 338.606684 0.006247 \n",
"2020-09-30 0.311580 321.342411 340.129589 0.037265 \n",
"2020-10-01 0.832955 327.202692 339.685308 0.041003 \n",
"2020-10-02 0.854917 331.050371 337.881629 0.015425 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spy = watch.load(\"SPY\")\n",
"spy.tail()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
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"nbformat": 4,
"nbformat_minor": 4
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