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
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

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

from watchlist import Watchlist
%pylab inline

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

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 = 09/09/2020, 22:54:24
ta = None

Common

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 = 09/09/2020, 22:54:24
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

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=None, created='09/09/2020, 22:54:24', last_run=None, run_time=None)

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=None, created='09/09/2020, 22:54:24', last_run=None, run_time=None)

Bad Strategy. (Misspelled Indicator)

In [6]:

# Misspelled indicator, will fail later when ran with Pandas
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=None, created='09/09/2020, 22:54:24', last_run=None, run_time=None)

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]:

watch = Watchlist(["SPY", "IWM"], ds=AV)

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

In [9]:

watch

Out [9]:

Watch(name='Watchlist: SPY, IWM', 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: object = None, **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.AllStrategy.
 |  
 |  Requirements:
 |  - Pandas TA (pip install pandas_ta)
 |  - AlphaVantage (pip install alphaVantage-api) for the Default Data Source.
 |      To use another Data Source, update the load() method after AV.
 |  
 |  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: object = None, **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 = ['dividend', 'split_coefficient'], file_path: str = '.', **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, timed=False)

[!] Loading All: SPY, IWM
[i] Loaded['D']: SPY_D.csv
[+] Strategy: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': False, 'append': True}
[i] Multiprocessing: 4 of 4 cores.
[i] Total indicators: 5
[i] Columns added: 5
[i] Loaded['D']: IWM_D.csv
[+] Strategy: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': False, 'append': True}
[i] Multiprocessing: 4 of 4 cores.
[i] Total indicators: 5
[i] Columns added: 5

In [12]:

watch.data

Out [12]:

{'SPY':                 open      high       low     close      volume   SMA_10  \
 date                                                                      
 1999-11-01  136.5000  137.0000  135.5625  135.5625   4006500.0      NaN   
 1999-11-02  135.9687  137.2500  134.5937  134.5937   6516900.0      NaN   
 1999-11-03  136.0000  136.3750  135.1250  135.5000   7222300.0      NaN   
 1999-11-04  136.7500  137.3593  135.7656  136.5312   7907500.0      NaN   
 1999-11-05  138.6250  139.1093  136.7812  137.8750   7431500.0      NaN   
 ...              ...       ...       ...       ...         ...      ...   
 2020-08-24  342.1200  343.0000  339.4504  342.9200  48588662.0  337.837   
 2020-08-25  343.5300  344.2100  342.2700  344.1200  38463381.0  338.969   
 2020-08-26  344.7600  347.8600  344.1700  347.5700  50790237.0  339.982   
 2020-08-27  348.5100  349.9000  346.5300  348.3300  58034142.0  341.132   
 2020-08-28  349.4400  350.7200  348.1500  350.5800  48588940.0  342.506   
 
               SMA_20    SMA_50    SMA_200  VOL_VOL_SMA_20  
 date                                                       
 1999-11-01       NaN       NaN        NaN             NaN  
 1999-11-02       NaN       NaN        NaN             NaN  
 1999-11-03       NaN       NaN        NaN             NaN  
 1999-11-04       NaN       NaN        NaN             NaN  
 1999-11-05       NaN       NaN        NaN             NaN  
 ...              ...       ...        ...             ...  
 2020-08-24  333.5285  322.2548  307.05540     51792231.45  
 2020-08-25  334.6760  322.9962  307.23510     50840651.55  
 2020-08-26  335.7985  323.6884  307.42825     50957455.45  
 2020-08-27  337.0170  324.4218  307.62815     50766076.85  
 2020-08-28  338.2200  325.1978  307.83605     48934986.10  
 
 [5241 rows x 10 columns],
 'IWM':               open    high      low   close      volume   SMA_10    SMA_20  \
 date                                                                         
 2000-05-26   91.06   91.44   90.630   91.44     37400.0      NaN       NaN   
 2000-05-30   92.75   94.81   92.750   94.81     28800.0      NaN       NaN   
 2000-05-31   95.13   96.38   95.130   95.75     18000.0      NaN       NaN   
 2000-06-01   97.11   97.31   97.110   97.31      3500.0      NaN       NaN   
 2000-06-02  101.70  102.40  101.700  102.40     14700.0      NaN       NaN   
 ...            ...     ...      ...     ...         ...      ...       ...   
 2020-08-31  157.19  157.37  155.300  155.43  17051511.0  155.956  155.9205   
 2020-09-01  155.22  157.31  154.450  157.21  15654144.0  156.038  156.2415   
 2020-09-02  157.96  158.98  156.175  158.46  16763449.0  156.244  156.4750   
 2020-09-03  158.12  158.29  152.960  153.78  32117585.0  156.046  156.4775   
 2020-09-04  155.71  155.89  149.290  152.80  30618783.0  155.865  156.3090   
 
               SMA_50    SMA_200  VOL_VOL_SMA_20  
 date                                             
 2000-05-26       NaN        NaN             NaN  
 2000-05-30       NaN        NaN             NaN  
 2000-05-31       NaN        NaN             NaN  
 2000-06-01       NaN        NaN             NaN  
 2000-06-02       NaN        NaN             NaN  
 ...              ...        ...             ...  
 2020-08-31  148.8200  145.82245     17493155.05  
 2020-09-01  149.1162  145.81800     17261612.35  
 2020-09-02  149.4254  145.81570     17173441.45  
 2020-09-03  149.7338  145.79200     17867433.35  
 2020-09-04  149.9808  145.76045     18374614.75  
 
 [5102 rows x 10 columns]}

In [13]:

watch.data["SPY"]

Out [13]:

	open	high	low	close	volume	SMA_10	SMA_20	SMA_50	SMA_200	VOL_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
...	...	...	...	...	...	...	...	...	...	...
2020-08-24	342.1200	343.0000	339.4504	342.9200	48588662.0	337.837	333.5285	322.2548	307.05540	51792231.45
2020-08-25	343.5300	344.2100	342.2700	344.1200	38463381.0	338.969	334.6760	322.9962	307.23510	50840651.55
2020-08-26	344.7600	347.8600	344.1700	347.5700	50790237.0	339.982	335.7985	323.6884	307.42825	50957455.45
2020-08-27	348.5100	349.9000	346.5300	348.3300	58034142.0	341.132	337.0170	324.4218	307.62815	50766076.85
2020-08-28	349.4400	350.7200	348.1500	350.5800	48588940.0	342.506	338.2200	325.1978	307.83605	48934986.10

5241 rows × 10 columns

In [ ]:

Easy to swap Strategies and run them

Running Simple Strategy A

In [14]:

# Load custom_a into Watchlist and verify
watch.strategy = custom_a
# watch.debug = True
watch.strategy

Out [14]:

Strategy(name='A', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}], description=None, created='09/09/2020, 22:54:24', last_run=None, run_time=None)

In [15]:

watch.load("IWM")

Out [15]:

[i] Loaded['D']: IWM_D.csv

	open	high	low	close	volume	SMA_50	SMA_200
date
2000-05-26	91.06	91.44	90.630	91.44	37400.0	NaN	NaN
2000-05-30	92.75	94.81	92.750	94.81	28800.0	NaN	NaN
2000-05-31	95.13	96.38	95.130	95.75	18000.0	NaN	NaN
2000-06-01	97.11	97.31	97.110	97.31	3500.0	NaN	NaN
2000-06-02	101.70	102.40	101.700	102.40	14700.0	NaN	NaN
...	...	...	...	...	...	...	...
2020-08-31	157.19	157.37	155.300	155.43	17051511.0	148.8200	145.82245
2020-09-01	155.22	157.31	154.450	157.21	15654144.0	149.1162	145.81800
2020-09-02	157.96	158.98	156.175	158.46	16763449.0	149.4254	145.81570
2020-09-03	158.12	158.29	152.960	153.78	32117585.0	149.7338	145.79200
2020-09-04	155.71	155.89	149.290	152.80	30618783.0	149.9808	145.76045

5102 rows × 7 columns

Running Simple Strategy B

In [16]:

# Load custom_b into Watchlist and verify
watch.strategy = custom_b
watch.strategy

Out [16]:

Strategy(name='B', ta=[{'kind': 'ema', 'length': 8}, {'kind': 'ema', 'length': 21}, {'kind': 'log_return', 'cumulative': True}, {'kind': 'rsi'}, {'kind': 'supertrend'}], description=None, created='09/09/2020, 22:54:24', last_run=None, run_time=None)

In [17]:

watch.load("SPY")

Out [17]:

[i] Loaded['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	NaN	NaN	0.000000	1	NaN	NaN
1999-11-02	135.9687	137.2500	134.5937	134.5937	6516900.0	NaN	NaN	-0.007172	0.000000	131.968750	1	131.968750	NaN
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	NaN	NaN	-0.000461	50.185503	131.968750	1	131.968750	NaN
1999-11-04	136.7500	137.3593	135.7656	136.5312	7907500.0	NaN	NaN	0.007120	69.153995	131.968750	1	131.968750	NaN
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	NaN	NaN	0.016915	79.896816	131.968750	1	131.968750	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-08-24	342.1200	343.0000	339.4504	342.9200	48588662.0	338.577882	333.520811	0.928064	72.830131	331.974175	1	331.974175	NaN
2020-08-25	343.5300	344.2100	342.2700	344.1200	38463381.0	339.809464	334.484374	0.931558	74.054445	334.479122	1	334.479122	NaN
2020-08-26	344.7600	347.8600	344.1700	347.5700	50790237.0	341.534027	335.673976	0.941533	77.231098	336.902819	1	336.902819	NaN
2020-08-27	348.5100	349.9000	346.5300	348.3300	58034142.0	343.044243	336.824524	0.943718	77.873776	338.960273	1	338.960273	NaN
2020-08-28	349.4400	350.7200	348.1500	350.5800	48588940.0	344.718856	338.075022	0.950156	79.700567	340.400948	1	340.400948	NaN

5241 rows × 13 columns

Running Bad Strategy. (Misspelled indicator)

In [18]:

# Load custom_run_failure into Watchlist and verify
watch.strategy = custom_run_failure
watch.strategy

Out [18]:

Strategy(name='Runtime Failure', ta=[{'kind': 'percet_return'}], description=None, created='09/09/2020, 22:54:24', last_run=None, run_time=None)

In [19]:

try:
    iwm = watch.load("IWM")
except AttributeError as error:
    print(f"[X] Oops! {error}")

[i] Loaded['D']: IWM_D.csv

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 [20]:

# 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 [20]:

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=None, created='09/09/2020, 22:54:24', last_run=None, run_time=None)

In [21]:

# Update the Watchlist
watch.strategy = vp_ma_chain_ta
watch.strategy.name

Out [21]:

'Volume MAs and Price MA chain'

In [22]:

spy = watch.load("SPY")
spy

Out [22]:

[i] Loaded['D']: SPY_D.csv

	open	high	low	close	volume	VOLUME_VOLUME_EMA_10	VOLUME_VOLUME_SMA_20	EMA_5	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
...	...	...	...	...	...	...	...	...	...
2020-08-24	342.1200	343.0000	339.4504	342.9200	48588662.0	4.999906e+07	51792231.45	339.779956	338.589691
2020-08-25	343.5300	344.2100	342.2700	344.1200	38463381.0	4.790167e+07	50840651.55	341.226637	339.658445
2020-08-26	344.7600	347.8600	344.1700	347.5700	50790237.0	4.842686e+07	50957455.45	343.341091	341.152455
2020-08-27	348.5100	349.9000	346.5300	348.3300	58034142.0	5.017364e+07	50766076.85	345.004061	342.852470
2020-08-28	349.4400	350.7200	348.1500	350.5800	48588940.0	4.988551e+07	48934986.10	346.862707	344.767837

5241 rows × 9 columns

In [ ]:

MACD BBANDS

In [23]:

# 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, "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 [23]:

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='09/09/2020, 22:54:24', last_run=None, run_time=None)

In [24]:

# Update the Watchlist
watch.strategy = macd_bands_ta
watch.strategy.name

Out [24]:

'MACD BBands'

In [25]:

spy = watch.load("SPY")
spy

Out [25]:

[i] Loaded['D']: SPY_D.csv

	open	high	low	close	volume	MACD_12_26_9	MACDh_12_26_9	MACDs_12_26_9	MACD_MACD_BBL_20_2.0	MACD_MACD_BBM_20_2.0	MACD_MACD_BBU_20_2.0
date
1999-11-01	136.5000	137.0000	135.5625	135.5625	4006500.0	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
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	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
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...
2020-08-24	342.1200	343.0000	339.4504	342.9200	48588662.0	5.341674	0.069881	5.271794	3.678022	4.914752	6.151482
2020-08-25	343.5300	344.2100	342.2700	344.1200	38463381.0	5.513019	0.192980	5.320039	3.807707	4.990945	6.174183
2020-08-26	344.7600	347.8600	344.1700	347.5700	50790237.0	5.859651	0.431690	5.427961	3.933776	5.083307	6.232838
2020-08-27	348.5100	349.9000	346.5300	348.3300	58034142.0	6.125079	0.557694	5.567385	4.100036	5.194943	6.289851
2020-08-28	349.4400	350.7200	348.1500	350.5800	48588940.0	6.442721	0.700269	5.742452	4.251657	5.319172	6.386687

5241 rows × 11 columns

In [ ]:

Comprehensive Strategy

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

In [26]:

momo_bands_sma_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"},
]
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 [26]:

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='09/09/2020, 22:54:24', last_run=None, run_time=None)

In [27]:

# Update the Watchlist
watch.strategy = momo_bands_sma_strategy
watch.strategy.name

Out [27]:

'Momo, Bands and SMAs and Cumulative Log Returns'

In [28]:

spy = watch.load("SPY", timed=True)
# Apply constants to the DataFrame for indicators
spy.ta.constants(True, [0, 30, 70])
spy.head(20)

Out [28]:

[i] Loaded['D']: SPY_D.csv
[i] Runtime: 1208.2109 ms (1.2082 s)

	open	high	low	close	volume	SMA_50	SMA_200	BBL_20_2.0	BBM_20_2.0	BBU_20_2.0	MACD_12_26_9	MACDh_12_26_9	MACDs_12_26_9	RSI_14	CUMLOGRET_1	SMA_5_CUMLOGRET_CUMLOGRET	0	30	70
date
1999-11-01	136.5000	137.0000	135.5625	135.5625	4006500.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	30	70
1999-11-02	135.9687	137.2500	134.5937	134.5937	6516900.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000000	-0.007172	NaN	0	30	70
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	50.185503	-0.000461	NaN	0	30	70
1999-11-04	136.7500	137.3593	135.7656	136.5312	7907500.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	69.153995	0.007120	NaN	0	30	70
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	79.896816	0.016915	NaN	0	30	70
1999-11-08	137.0000	138.3750	136.7500	138.0000	4649200.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	80.574537	0.017821	0.006845	0	30	70
1999-11-09	138.5000	138.6875	136.2812	136.7031	4533700.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	58.528352	0.008379	0.009955	0	30	70
1999-11-10	136.2500	138.3906	136.0781	137.7187	6405600.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	66.303684	0.015780	0.013203	0	30	70
1999-11-11	138.1875	138.5000	137.4687	138.5000	4794100.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	70.833962	0.021438	0.016066	0	30	70
1999-11-12	139.2500	139.9843	137.1250	139.7500	11802900.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	76.319408	0.030422	0.018768	0	30	70
1999-11-15	139.8437	140.2500	139.4062	140.0781	2187500.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	77.514804	0.032767	0.021757	0	30	70
1999-11-16	140.5625	143.0000	140.0937	141.2500	7544800.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	81.170904	0.041099	0.028301	0	30	70
1999-11-17	142.2500	142.9375	141.3125	141.6250	9459000.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	82.169980	0.043750	0.033895	0	30	70
1999-11-18	142.4375	143.0000	141.6250	142.6250	4491000.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	84.527630	0.050786	0.039765	0	30	70
1999-11-19	142.4062	142.9687	142.0000	142.5000	4832100.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	83.049344	0.049909	0.043662	0	30	70
1999-11-22	142.4375	143.0000	141.5000	142.4687	4155400.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	82.659517	0.049690	0.047047	0	30	70
1999-11-23	142.8437	142.8437	140.3750	141.2187	5918000.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	68.775385	0.040877	0.047002	0	30	70
1999-11-24	140.7500	142.4375	140.0000	141.9687	4459700.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	71.832489	0.046174	0.047487	0	30	70
1999-11-26	142.4687	142.8750	141.2500	141.4375	1693900.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	66.840920	0.042425	0.045815	0	30	70
1999-11-29	140.8750	141.9218	140.4375	140.9375	7348600.0	NaN	NaN	134.086598	139.34217	144.597742	NaN	NaN	0.0	62.442534	0.038884	0.043610	0	30	70

In [ ]:

94 KiB Raw Blame History Unescape Escape

Pandas TA (pandas_ta) Strategies for Custom Technical Analysis

Topics

What is a Pandas TA Strategy?

Strategy Requirements:

Optional Requirements:

Things to note:

Builtin Examples

All

Common

Creating Strategies

Simple Strategy A

Simple Strategy B

Bad Strategy. (Misspelled Indicator)

Strategy Management and Execution with Watchlist

Initialize AlphaVantage Data Source

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

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

Help about Watchlist

Default Strategy is "Common"

Easy to swap Strategies and run them

Running Simple Strategy A

Running Simple Strategy B

Running Bad Strategy. (Misspelled indicator)

Indicator Composition/Chaining

Volume MAs and MA chains

MACD BBANDS

Comprehensive Strategy

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

94 KiB

Raw Blame History