wassname/pandas-ta
1
0
Fork 0
mirror of https://github.com/wassname/pandas-ta.git synced 2026-07-17 11:31:03 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
e09ebd8441dfd6ecda2972832bc0d97aeb00d758
pandas-ta/examples/PandasTA_Strategy_Examples.ipynb
T

207 KiB
Raw Blame History

Pandas TA (pandas_ta) Strategies for Custom Technical Analysis

Topics

  • What is a Pandas TA Strategy?
    • Builtin Strategies: AllStrategy and CommonStrategy
    • Creating Strategies
  • Watchlist Class
    • Strategy Management and Execution
    • 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.
  • Indicator Composition/Chaining for more Complex Strategies
    • Comprehensive Example: MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns
In [1]:
%matplotlib inline
import datetime as dt

from tqdm import tqdm

import pandas as pd
import pandas_ta as ta
from alphaVantageAPI.alphavantage import AlphaVantage  # pip install alphaVantage-api

from watchlist import Watchlist # Is this failing? If so, copy it locally. See above.

print(f"\nPandas TA v{ta.version}\nTo install the Latest Version:\n$ pip install -U git+https://github.com/twopirllc/pandas-ta\n")
%pylab inline
Pandas TA v0.2.81b0
To install the Latest Version:
$ pip install -U git+https://github.com/twopirllc/pandas-ta

Populating the interactive namespace from numpy and matplotlib

What is a Pandas TA Strategy?

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.

Strategy Requirements:

  • name: Some short memorable string. Note: Case-insensitive "All" is reserved.
  • ta: A list of dicts containing keyword arguments to identify the indicator and the indicator's arguments

Optional Requirements:

  • description: A more detailed description of what the Strategy tries to capture. Default: None
  • created: At datetime string of when it was created. Default: Automatically generated.

Things to note:

  • A Strategy will fail when consumed by Pandas TA if there is no {"kind": "indicator name"} attribute.

Builtin Examples

All

Default Values

In [2]:
AllStrategy = ta.AllStrategy
print("name =", AllStrategy.name)
print("description =", AllStrategy.description)
print("created =", AllStrategy.created)
print("ta =", AllStrategy.ta)
name = All
description = All the indicators with their default settings. Pandas TA default.
created = Wednesday May 26, 2021, NYSE: 10:57:12, Local: 14:57:12 PDT, Day 146/365 (40.00%)
ta = None

Common

Default Values

In [3]:
CommonStrategy = ta.CommonStrategy
print("name =", CommonStrategy.name)
print("description =", CommonStrategy.description)
print("created =", CommonStrategy.created)
print("ta =", CommonStrategy.ta)
name = Common Price and Volume SMAs
description = Common Price SMAs: 10, 20, 50, 200 and Volume SMA: 20.
created = Wednesday May 26, 2021, NYSE: 10:57:12, Local: 14:57:12 PDT, Day 146/365 (40.00%)
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'}]
In [ ]:

Creating Strategies

Strategies require a name and an array of dicts containing the "kind" of indicator ("sma") and other potential parameters for ta.

Simple Strategy A

In [4]:
custom_a = ta.Strategy(name="A", ta=[{"kind": "sma", "length": 50}, {"kind": "sma", "length": 200}])
custom_a
Out [4]:
Strategy(name='A', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}], description='TA Description', created='Wednesday May 26, 2021, NYSE: 10:57:12, Local: 14:57:12 PDT, Day 146/365 (40.00%)')

Simple Strategy B

In [5]:
custom_b = ta.Strategy(name="B", ta=[{"kind": "ema", "length": 8}, {"kind": "ema", "length": 21}, {"kind": "log_return", "cumulative": True}, {"kind": "rsi"}, {"kind": "supertrend"}])
custom_b
Out [5]:
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='Wednesday May 26, 2021, NYSE: 10:57:12, Local: 14:57:12 PDT, Day 146/365 (40.00%)')

Bad Strategy. (Misspelled Indicator)

In [6]:
# Misspelled indicator, will fail later when ran with Pandas TA
custom_run_failure = ta.Strategy(name="Runtime Failure", ta=[{"kind": "percet_return"}])
custom_run_failure
Out [6]:
Strategy(name='Runtime Failure', ta=[{'kind': 'percet_return'}], description='TA Description', created='Wednesday May 26, 2021, NYSE: 10:57:12, Local: 14:57:12 PDT, Day 146/365 (40.00%)')
In [ ]:

Strategy Management and Execution with Watchlist

Initialize AlphaVantage Data Source

In [7]:
AV = AlphaVantage(
    api_key="YOUR API KEY", premium=False,
    output_size='full', clean=True,
    export_path=".", export=True
)
AV
Out [7]:
AlphaVantage(
  end_point:str = https://www.alphavantage.co/query,
  api_key:str = YOUR API KEY,
  export:bool = True,
  export_path:str = .,
  output_size:str = full,
  output:str = csv,
  datatype:str = json,
  clean:bool = True,
  proxy:dict = {}
)

Create Watchlist and set it's 'ds' to AlphaVantage

In [8]:
data_source = "av" # Default
# data_source = "yahoo"
watch = Watchlist(["SPY", "IWM"], ds_name=data_source, timed=False)

Info about the Watchlist. Note, the default Strategy is "All"

In [9]:
watch
Out [9]:
Watch(name='Watch: SPY, IWM', ds_name='av', tickers[2]='SPY, IWM', tf='D', strategy[5]='Common Price and Volume SMAs')

Help about Watchlist

In [10]:
help(Watchlist)
Help on class Watchlist in module watchlist:

class Watchlist(builtins.object)
 |  Watchlist(tickers: list, tf: str = None, name: str = None, strategy: pandas_ta.core.Strategy = None, ds_name: str = 'av', **kwargs)
 |  
 |  # Watchlist Class (** This is subject to change! **)
 |  A simple Class to load/download financial market data and automatically
 |  apply Technical Analysis indicators with a Pandas TA Strategy.
 |  
 |  Default Strategy: pandas_ta.CommonStrategy
 |  
 |  ## Package Support:
 |  ### Data Source (Default: AlphaVantage)
 |  - AlphaVantage (pip install alphaVantage-api).
 |  - Python Binance (pip install python-binance). # Future Support
 |  - Yahoo Finance (pip install yfinance). # Almost Supported
 |  
 |  # Technical Analysis:
 |  - Pandas TA (pip install pandas_ta)
 |  
 |  ## Required Arguments:
 |  - tickers: A list of strings containing tickers. Example: ["SPY", "AAPL"]
 |  
 |  Methods defined here:
 |  
 |  __init__(self, tickers: list, tf: str = None, name: str = None, strategy: pandas_ta.core.Strategy = None, ds_name: str = 'av', **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |  
 |  __repr__(self) -> str
 |      Return repr(self).
 |  
 |  indicators(self, *args, **kwargs) -> <built-in function any>
 |      Returns the list of indicators that are available with Pandas Ta.
 |  
 |  load(self, ticker: str = None, tf: str = None, index: str = 'date', drop: list = [], plot: bool = False, **kwargs) -> pandas.core.frame.DataFrame
 |      Loads or Downloads (if a local csv does not exist) the data from the
 |      Data Source. When successful, it returns a Data Frame for the requested
 |      ticker. If no tickers are given, it loads all the tickers.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  data
 |      When not None, it contains a dictionary of DataFrames keyed by ticker. data = {"SPY": pd.DataFrame, ...}
 |  
 |  name
 |      The name of the Watchlist. Default: "Watchlist: {Watchlist.tickers}".
 |  
 |  strategy
 |      Sets a valid Strategy. Default: pandas_ta.CommonStrategy
 |  
 |  tf
 |      Alias for timeframe. Default: 'D'
 |  
 |  tickers
 |      tickers
 |      
 |      If a string, it it converted to a list. Example: "AAPL" -> ["AAPL"]
 |          * Does not accept, comma seperated strings.
 |      If a list, checks if it is a list of strings.
 |  
 |  verbose
 |      Toggle the verbose property. Default: False

Default Strategy is "Common"

In [11]:
# No arguments loads all the tickers and applies the Strategy to each ticker.
# The result can be accessed with Watchlist's 'data' property which returns a 
# dictionary keyed by ticker and DataFrames as values 
watch.load(verbose=True)
[!] Loading All: SPY, IWM
[+] Downloading[av]: SPY[D]
[+] Strategy: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': False, 'append': True}
[i] Multiprocessing 5 indicators with 7 chunks and 8/8 cpus.
[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Wednesday May 26, 2021, NYSE: 10:57:15, Local: 14:57:15 PDT, Day 146/365 (40.00%)
[+] Downloading[av]: IWM[D]
[+] Strategy: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': False, 'append': True}
[i] Multiprocessing 5 indicators with 7 chunks and 8/8 cpus.
[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Wednesday May 26, 2021, NYSE: 10:57:32, Local: 14:57:32 PDT, Day 146/365 (40.00%)
In [12]:
", ".join([f"{t}: {d.shape}" for t,d in watch.data.items()])
Out [12]:
'SPY: (5427, 10), IWM: (5283, 10)'
In [13]:
watch.data["SPY"]
Out [13]:
open high low close volume SMA_10 SMA_20 SMA_50 SMA_200 VOL_SMA_20
date
1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN NaN NaN NaN NaN
1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN NaN NaN NaN NaN
1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN NaN NaN NaN NaN
1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN NaN NaN NaN NaN
1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ...
2021-05-20 411.8000 416.6250 411.6700 415.2800 78022218.0 414.014 415.7705 407.6032 371.10540 78769145.90
2021-05-21 416.8700 418.2000 414.4500 414.9400 76578662.0 413.296 415.6805 408.0314 371.51955 78934269.25
2021-05-24 417.3400 420.3200 417.0800 419.1700 51376702.0 413.419 415.7585 408.5336 371.94375 78893984.75
2021-05-25 420.3300 420.7100 417.6200 418.2400 57451396.0 413.822 415.7945 408.9702 372.36210 79201401.75
2021-05-26 418.8700 419.6100 417.7600 419.0700 42955732.0 415.188 415.8780 409.4334 372.77960 78787245.65

5427 rows × 10 columns

In [ ]:
In [14]:
watch.load("SPY", plot=True, mas=True)
Out [14]:
[i] Loaded SPY[D]: SPY_D.csv
open high low close volume SMA_10 SMA_20 SMA_50 SMA_200 VOL_SMA_20
date
1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN NaN NaN NaN NaN
1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN NaN NaN NaN NaN
1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN NaN NaN NaN NaN
1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN NaN NaN NaN NaN
1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ...
2021-05-20 411.8000 416.6250 411.6700 415.2800 78022218.0 414.014 415.7705 407.6032 371.10540 78769145.90
2021-05-21 416.8700 418.2000 414.4500 414.9400 76578662.0 413.296 415.6805 408.0314 371.51955 78934269.25
2021-05-24 417.3400 420.3200 417.0800 419.1700 51376702.0 413.419 415.7585 408.5336 371.94375 78893984.75
2021-05-25 420.3300 420.7100 417.6200 418.2400 57451396.0 413.822 415.7945 408.9702 372.36210 79201401.75
2021-05-26 418.8700 419.6100 417.7600 419.0700 42955732.0 415.188 415.8780 409.4334 372.77960 78787245.65

5427 rows × 10 columns

In [ ]:

Easy to swap Strategies and run them

Running Simple Strategy A

In [15]:
# Load custom_a into Watchlist and verify
watch.strategy = custom_a
# watch.debug = True
watch.strategy
Out [15]:
Strategy(name='A', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}], description='TA Description', created='Wednesday May 26, 2021, NYSE: 10:57:12, Local: 14:57:12 PDT, Day 146/365 (40.00%)')
In [16]:
watch.load("IWM")
Out [16]:
[i] Loaded IWM[D]: IWM_D.csv
open high low close volume SMA_50 SMA_200
date
2000-05-26 91.06 91.44 90.6300 91.44 37400.0 NaN NaN
2000-05-30 92.75 94.81 92.7500 94.81 28800.0 NaN NaN
2000-05-31 95.13 96.38 95.1300 95.75 18000.0 NaN NaN
2000-06-01 97.11 97.31 97.1100 97.31 3500.0 NaN NaN
2000-06-02 101.70 102.40 101.7000 102.40 14700.0 NaN NaN
... ... ... ... ... ... ... ...
2021-05-20 218.27 219.87 216.3300 219.40 24932752.0 222.9280 192.22100
2021-05-21 221.33 222.37 219.3497 219.97 24213723.0 222.6830 192.55190
2021-05-24 221.18 222.45 220.0000 221.40 18324181.0 222.4392 192.89025
2021-05-25 222.23 223.71 219.1900 219.26 20514901.0 222.1360 193.20570
2021-05-26 220.13 223.69 220.1200 223.35 20314326.0 221.9930 193.53350

5283 rows × 7 columns

Running Simple Strategy B

In [17]:
# Load custom_b into Watchlist and verify
watch.strategy = custom_b
watch.strategy
Out [17]:
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='Wednesday May 26, 2021, NYSE: 10:57:12, Local: 14:57:12 PDT, Day 146/365 (40.00%)')
In [18]:
watch.load("SPY")
Out [18]:
[i] Loaded SPY[D]: SPY_D.csv
open high low close volume EMA_8 EMA_21 CUMLOGRET_1 RSI_14 SUPERT_7_3.0 SUPERTd_7_3.0 SUPERTl_7_3.0 SUPERTs_7_3.0
date
1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN NaN 0.000000 NaN 0.000000 1 NaN NaN
1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN NaN -0.007172 NaN NaN 1 NaN NaN
1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN NaN -0.000461 NaN NaN 1 NaN NaN
1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN NaN 0.007120 NaN NaN 1 NaN NaN
1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN NaN 0.016915 NaN NaN 1 NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2021-05-20 411.8000 416.6250 411.6700 415.2800 78022218.0 413.742645 413.643188 1.119520 53.190381 422.422132 -1 NaN 422.422132
2021-05-21 416.8700 418.2000 414.4500 414.9400 76578662.0 414.008724 413.761080 1.118701 52.748699 422.422132 -1 NaN 422.422132
2021-05-24 417.3400 420.3200 417.0800 419.1700 51376702.0 415.155674 414.252800 1.128844 57.479369 422.422132 -1 NaN 422.422132
2021-05-25 420.3300 420.7100 417.6200 418.2400 57451396.0 415.841080 414.615272 1.126623 56.148383 422.422132 -1 NaN 422.422132
2021-05-26 418.8700 419.6100 417.7600 419.0700 42955732.0 416.558618 415.020248 1.128605 57.103084 422.422132 -1 NaN 422.422132

5427 rows × 13 columns

Running Bad Strategy. (Misspelled indicator)

In [19]:
# Load custom_run_failure into Watchlist and verify
watch.strategy = custom_run_failure
watch.strategy
Out [19]:
Strategy(name='Runtime Failure', ta=[{'kind': 'percet_return'}], description='TA Description', created='Wednesday May 26, 2021, NYSE: 10:57:12, Local: 14:57:12 PDT, Day 146/365 (40.00%)')
In [20]:
try:
    iwm = watch.load("IWM")
except AttributeError as error:
    print(f"[X] Oops! {error}")
[i] Loaded IWM[D]: IWM_D.csv
[X] Oops! 'AnalysisIndicators' object has no attribute 'percet_return'
In [ ]:

Indicator Composition/Chaining

  • When you need an indicator to depend on the value of a prior indicator
  • Utilitze prefix or suffix to help identify unique columns or avoid column name clashes.

Volume MAs and MA chains

In [21]:
# Set EMA's and SMA's 'close' to 'volume' to create Volume MAs, prefix 'volume' MAs with 'VOLUME' so easy to identify the column
# Take a price EMA and apply LINREG from EMA's output
volmas_price_ma_chain = [
    {"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"},
]
vp_ma_chain_ta = ta.Strategy("Volume MAs and Price MA chain", volmas_price_ma_chain)
vp_ma_chain_ta
Out [21]:
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='Wednesday May 26, 2021, NYSE: 10:57:12, Local: 14:57:12 PDT, Day 146/365 (40.00%)')
In [22]:
# Update the Watchlist
watch.strategy = vp_ma_chain_ta
watch.strategy.name
Out [22]:
'Volume MAs and Price MA chain'
In [23]:
spy = watch.load("SPY")
spy
Out [23]:
[i] Loaded SPY[D]: SPY_D.csv
open high low close volume VOLUME_EMA_10 VOLUME_SMA_20 EMA_5 EMA_5_LR_8
date
1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN NaN NaN NaN
1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN NaN NaN NaN
1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN NaN NaN NaN
1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN NaN NaN NaN
1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN NaN 136.012480 NaN
... ... ... ... ... ... ... ... ... ...
2021-05-20 411.8000 416.6250 411.6700 415.2800 78022218.0 8.461273e+07 78769145.90 413.514631 413.070731
2021-05-21 416.8700 418.2000 414.4500 414.9400 76578662.0 8.315199e+07 78934269.25 413.989754 413.618305
2021-05-24 417.3400 420.3200 417.0800 419.1700 51376702.0 7.737467e+07 78893984.75 415.716503 414.370775
2021-05-25 420.3300 420.7100 417.6200 418.2400 57451396.0 7.375225e+07 79201401.75 416.557668 415.115397
2021-05-26 418.8700 419.6100 417.7600 419.0700 42955732.0 6.815289e+07 78787245.65 417.395112 416.089074

5427 rows × 9 columns

In [ ]:

MACD BBANDS

In [24]:
# MACD is the initial indicator that BBANDS depends on.
# 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
macd_bands_ta = [
    {"kind":"macd"},
    {"kind":"bbands", "close": "MACD_12_26_9", "length": 20, "ddof": 0, "prefix": "MACD"}
]
macd_bands_ta = ta.Strategy("MACD BBands", macd_bands_ta, f"BBANDS_{macd_bands_ta[1]['length']} applied to MACD")
macd_bands_ta
Out [24]:
Strategy(name='MACD BBands', ta=[{'kind': 'macd'}, {'kind': 'bbands', 'close': 'MACD_12_26_9', 'length': 20, 'ddof': 0, 'prefix': 'MACD'}], description='BBANDS_20 applied to MACD', created='Wednesday May 26, 2021, NYSE: 10:57:12, Local: 14:57:12 PDT, Day 146/365 (40.00%)')
In [25]:
# Update the Watchlist
watch.strategy = macd_bands_ta
watch.strategy.name
Out [25]:
'MACD BBands'
In [26]:
spy = watch.load("SPY")
spy
Out [26]:
[i] Loaded SPY[D]: SPY_D.csv
open high low close volume MACD_12_26_9 MACDh_12_26_9 MACDs_12_26_9 MACD_BBL_20_2.0 MACD_BBM_20_2.0 MACD_BBU_20_2.0 MACD_BBB_20_2.0
date
1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN NaN NaN NaN NaN NaN NaN
1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN NaN NaN NaN NaN NaN NaN
1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN NaN NaN NaN NaN NaN NaN
1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN NaN NaN NaN NaN NaN NaN
1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ...
2021-05-20 411.8000 416.6250 411.6700 415.2800 78022218.0 1.189878 -1.043818 2.233696 7.451354 3.971502 0.491649 -175.241148
2021-05-21 416.8700 418.2000 414.4500 414.9400 76578662.0 1.171810 -0.849509 2.021319 7.279997 3.729337 0.178677 -190.417786
2021-05-24 417.3400 420.3200 417.0800 419.1700 51376702.0 1.481736 -0.431666 1.913402 7.021896 3.503329 -0.015238 -200.869906
2021-05-25 420.3300 420.7100 417.6200 418.2400 57451396.0 1.633482 -0.223936 1.857418 6.714411 3.289206 -0.136000 -208.269455
2021-05-26 418.8700 419.6100 417.7600 419.0700 42955732.0 1.799967 -0.045961 1.845928 6.374729 3.090622 -0.193485 -212.520806

5427 rows × 12 columns

In [ ]:

Comprehensive Strategy

MACD and RSI Momentum with BBANDS and SMAs and Cumulative Log Returns

In [27]:
momo_bands_sma_ta = [
    {"kind":"sma", "length": 50},
    {"kind":"sma", "length": 200},
    {"kind":"bbands", "length": 20, "ddof": 0},
    {"kind":"macd"},
    {"kind":"rsi"},
    {"kind":"log_return", "cumulative": True},
    {"kind":"sma", "close": "CUMLOGRET_1", "length": 5, "suffix": "CUMLOGRET"},
]
momo_bands_sma_strategy = ta.Strategy(
    "Momo, Bands and SMAs and Cumulative Log Returns", # name
    momo_bands_sma_ta, # ta
    "MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns" # description
)
momo_bands_sma_strategy
Out [27]:
Strategy(name='Momo, Bands and SMAs and Cumulative Log Returns', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}, {'kind': 'bbands', 'length': 20, 'ddof': 0}, {'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='Wednesday May 26, 2021, NYSE: 10:57:12, Local: 14:57:12 PDT, Day 146/365 (40.00%)')
In [28]:
# Update the Watchlist
watch.strategy = momo_bands_sma_strategy
watch.strategy.name
Out [28]:
'Momo, Bands and SMAs and Cumulative Log Returns'
In [29]:
spy = watch.load("SPY")
# Apply constants to the DataFrame for indicators
spy.ta.constants(True, [0, 30, 70])
spy.tail()
Out [29]:
[i] Loaded SPY[D]: SPY_D.csv
open high low close volume SMA_50 SMA_200 BBL_20_2.0 BBM_20_2.0 BBU_20_2.0 BBB_20_2.0 MACD_12_26_9 MACDh_12_26_9 MACDs_12_26_9 RSI_14 CUMLOGRET_1 SMA_5_CUMLOGRET 0 30 70
date
2021-05-20 411.80 416.625 411.67 415.28 78022218.0 407.6032 371.10540 423.149919 415.7705 408.391081 -3.549756 1.189878 -1.043818 2.233696 53.190381 1.119520 1.116506 0 30 70
2021-05-21 416.87 418.200 414.45 414.94 76578662.0 408.0314 371.51955 423.054331 415.6805 408.306669 -3.547836 1.171810 -0.849509 2.021319 52.748699 1.118701 1.115717 0 30 70
2021-05-24 417.34 420.320 417.08 419.17 51376702.0 408.5336 371.94375 423.244472 415.7585 408.272528 -3.601115 1.481736 -0.431666 1.913402 57.479369 1.128844 1.117466 0 30 70
2021-05-25 420.33 420.710 417.62 418.24 57451396.0 408.9702 372.36210 423.320826 415.7945 408.268174 -3.620214 1.633482 -0.223936 1.857418 56.148383 1.126623 1.120501 0 30 70
2021-05-26 418.87 419.610 417.76 419.07 42955732.0 409.4334 372.77960 423.510034 415.8780 408.245966 -3.670324 1.799967 -0.045961 1.845928 57.103084 1.128605 1.124459 0 30 70
In [ ]:

Additional Strategy Options

The params keyword takes a tuple as a shorthand to the parameter arguments in order.

  • Note: If the indicator arguments change, so will results. Breaking Changes will always be posted on the README.

The col_numbers keyword takes a tuple specifying which column to return if the result is a DataFrame.

In [30]:
params_ta = [
    {"kind":"ema", "params": (10,)},
    # params sets MACD's keyword arguments: fast=9, slow=19, signal=10
    # and returning the 2nd column: histogram
    {"kind":"macd", "params": (9, 19, 10), "col_numbers": (1,)},
    # Selects the Lower and Upper Bands and renames them LB and UB, ignoring the MB
    {"kind":"bbands", "col_numbers": (0,2), "col_names": ("LB", "UB")},
    {"kind":"log_return", "params": (5, False)},
]
params_ta_strategy = ta.Strategy(
    "EMA, MACD History, Outter BBands, Log Returns", # name
    params_ta, # ta
    "EMA, MACD History, BBands(LB, UB), and Log Returns Strategy" # description
)
params_ta_strategy
Out [30]:
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='Wednesday May 26, 2021, NYSE: 10:57:12, Local: 14:57:12 PDT, Day 146/365 (40.00%)')
In [31]:
# Update the Watchlist
watch.strategy = params_ta_strategy
watch.strategy.name
Out [31]:
'EMA, MACD History, Outter BBands, Log Returns'
In [32]:
spy = watch.load("SPY")
spy.tail()
Out [32]:
[i] Loaded SPY[D]: SPY_D.csv
open high low close volume EMA_10 MACDh_9_19_10 LB UB LOGRET_5
date
2021-05-20 411.80 416.625 411.67 415.28 78022218.0 413.931044 -0.745073 418.481395 409.590605 0.012113
2021-05-21 416.87 418.200 414.45 414.94 76578662.0 414.114490 -0.506287 417.556042 409.859958 -0.003945
2021-05-24 417.34 420.320 417.08 419.17 51376702.0 415.033674 -0.003415 420.261736 408.614264 0.008746
2021-05-25 420.33 420.710 417.62 418.24 57451396.0 415.616642 0.202577 421.540745 409.855255 0.015178
2021-05-26 418.87 419.610 417.76 419.07 42955732.0 416.244526 0.362725 421.044635 413.635365 0.019785
In [ ]:

Disclaimer

  • All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, or individual’s trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.

  • Any opinions, news, research, analyses, prices, or other information offered is provided as general market commentary, and does not constitute investment advice. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from use of or reliance on such information.

Reference in New Issue View Git Blame Copy Permalink