386

Github GitHub - twopirllc/pandas-ta: Technical Analysis Indicators - Pandas TA i...

 1 year ago
source link: https://github.com/twopirllc/pandas-ta
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.

Pandas TA - A Technical Analysis Library in Python 3

Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas library with more than 130 Indicators and Utility functions. Many commonly used indicators are included, such as: Simple Moving Average (sma) Moving Average Convergence Divergence (macd), Hull Exponential Moving Average (hma), Bollinger Bands (bbands), On-Balance Volume (obv), Aroon & Aroon Oscillator (aroon), Squeeze (squeeze) and many more.

Table of contents

Features

  • Has 130+ indicators and utility functions.
  • Indicators are tightly correlated with the de facto TA Lib if they share common indicators.
  • Have the need for speed? By using the DataFrame strategy method, you get multiprocessing for free!
  • Easily add prefixes or suffixes or both to columns names. Useful for Custom Chained Strategies.
  • Example Jupyter Notebooks under the examples directory, including how to create Custom Strategies using the new Strategy Class
  • Potential Data Leaks: ichimoku and dpo. See indicator list below for details.
  • UNDER DEVELOPMENT: Performance Metrics
  • UNDER DEVELOPMENT: Easy Downloading of ohlcv data using yfinance. See help(ta.ticker) and help(ta.yf)

Installation

Stable

The pip version is the last most stable release. Version: 0.2.45b

$ pip install pandas_ta

Latest Version

Best choice! Version: 0.2.67b

$ pip install -U git+https://github.com/twopirllc/pandas-ta

Cutting Edge

This is the Development Version which could have bugs and other undesireable side effects. Use at own risk!

$ pip install -U git+https://github.com/twopirllc/pandas-ta.git@development

Quick Start

import pandas as pd
import pandas_ta as ta

df = pd.DataFrame() # Empty DataFrame

# Load data
df = pd.read_csv("path/to/symbol.csv", sep=",")
# OR if you have yfinance installed
df = df.ta.ticker("aapl")

# VWAP requires the DataFrame index to be a DatetimeIndex.
# Replace "datetime" with the appropriate column from your DataFrame
df.set_index(pd.DatetimeIndex(df["datetime"]), inplace=True)

# Calculate Returns and append to the df DataFrame
df.ta.log_return(cumulative=True, append=True)
df.ta.percent_return(cumulative=True, append=True)

# New Columns with results
df.columns

# Take a peek
df.tail()

# vv Continue Post Processing vv

Help

import pandas as pd
import pandas_ta as ta

# Create a DataFrame so 'ta' can be used.
df = pd.DataFrame()

# Help about this, 'ta', extension
help(df.ta)

# List of all indicators
df.ta.indicators()

# Help about an indicator such as bbands
help(ta.bbands)

Issues and Contributions

Thanks for using Pandas TA!

  • Comments and Feedback

    • Have you read this document?
    • Are you running the latest version?
      • $ pip install -U git+https://github.com/twopirllc/pandas-ta
    • Have you tried the Examples?
      • Did they help?
      • What is missing?
      • Could you help improve them?
    • Did you know you can easily build Custom Strategies with the Strategy Class?
    • Documentation could always be improved. Can you help contribute?
  • Bugs, Indicators or Feature Requests

    • First, search the Closed Issues before you Open a new Issue; it may have already been solved.
    • Please be as detailed as possible with reproducible code, links if any, applicable screenshots, errors, logs, and data samples. You will be asked again if you provide nothing.
      • You want a new indicator not currently listed.
      • You want an alternate version of an existing indicator.
      • The indicator does not match another website, library, broker platform, language, et al.
        • Do you have correlation analysis to back your claim?
        • Can you contribute?
    • You will be asked to fill out an Issue even if you email my personal email address.

Contributors

Thank you for your contributions!

alexonab | allahyarzadeh | codesutras | DrPaprikaa | daikts | dorren | edwardwang1 | ffhirata | FGU1 | lluissalord | luisbarrancos |M6stafa | maxdignan | mchant | moritzgun | NkosenhleDuma | pbrumblay | RajeshDhalange | rengel8 | rluong003 | SoftDevDanial | tg12 | twrobel | whubsch | witokondoria | wouldayajustlookatit | YuvalWein

Programming Conventions

Pandas TA has three primary "styles" of processing Technical Indicators for your use case and/or requirements. They are: Standard, DataFrame Extension, and the Pandas TA Strategy. Each with increasing levels of abstraction for ease of use. As you become more familiar with Pandas TA, the simplicity and speed of using a Pandas TA Strategy may become more apparent. Furthermore, you can create your own indicators through Chaining or Composition. Lastly, each indicator either returns a Series or a DataFrame in Uppercase Underscore format regardless of style.

Standard

You explicitly define the input columns and take care of the output.

  • sma10 = ta.sma(df["Close"], length=10)
    • Returns a Series with name: SMA_10
  • donchiandf = ta.donchian(df["HIGH"], df["low"], lower_length=10, upper_length=15)
    • Returns a DataFrame named DC_10_15 and column names: DCL_10_15, DCM_10_15, DCU_10_15
  • ema10_ohlc4 = ta.ema(ta.ohlc4(df["Open"], df["High"], df["Low"], df["Close"]), length=10)
    • Chaining indicators is possible but you have to be explicit.
    • Since it returns a Series named EMA_10. If needed, you may need to uniquely name it.

Pandas TA DataFrame Extension

Calling df.ta will automatically lowercase OHLCVA to ohlcva: open, high, low, close, volume, adj_close. By default, df.ta will use the ohlcva for the indicator arguments removing the need to specify input columns directly.

  • sma10 = df.ta.sma(length=10)
    • Returns a Series with name: SMA_10
  • ema10_ohlc4 = df.ta.ema(close=df.ta.ohlc4(), length=10, suffix="OHLC4")
    • Returns a Series with name: EMA_10_OHLC4
    • Chaining Indicators require specifying the input like: close=df.ta.ohlc4().
  • donchiandf = df.ta.donchian(lower_length=10, upper_length=15)
    • Returns a DataFrame named DC_10_15 and column names: DCL_10_15, DCM_10_15, DCU_10_15

Same as the last three examples, but appending the results directly to the DataFrame df.

  • df.ta.sma(length=10, append=True)
    • Appends to df column name: SMA_10.
  • df.ta.ema(close=df.ta.ohlc4(append=True), length=10, suffix="OHLC4", append=True)
    • Chaining Indicators require specifying the input like: close=df.ta.ohlc4().
  • df.ta.donchian(lower_length=10, upper_length=15, append=True)
    • Appends to df with column names: DCL_10_15, DCM_10_15, DCU_10_15.

Pandas TA Strategy

A Pandas TA Strategy is a named group of indicators to be run by the strategy method. All Strategies use mulitprocessing except when using the col_names parameter (see below). There are different types of Strategies listed in the following section.

Here are the previous Styles implemented using a Strategy Class:

# (1) Create the Strategy
MyStrategy = ta.Strategy(
    name="DCSMA10",
    ta=[
        {"kind": "ohlc4"},
        {"kind": "sma", "length": 10},
        {"kind": "donchian", "lower_length": 10, "upper_length": 15},
        {"kind": "ema", "close": "OHLC4", "length": 10, "suffix": "OHLC4"},
    ]
)

# (2) Run the Strategy
df.ta.strategy(MyStrategy, **kwargs)

Pandas TA Strategies

The Strategy Class is a simple way to name and group your favorite TA Indicators by using a Data Class. Pandas TA comes with two prebuilt basic Strategies to help you get started: AllStrategy and CommonStrategy. A Strategy can be as simple as the CommonStrategy or as complex as needed using Composition/Chaining.

  • When using the strategy method, all indicators will be automatically appended to the DataFrame df.
  • You are using a Chained Strategy when you have the output of one indicator as input into one or more indicators in the same Strategy.
  • Note: Use the 'prefix' and/or 'suffix' keywords to distinguish the composed indicator from it's default Series.

See the Pandas TA Strategy Examples Notebook for examples including Indicator Composition/Chaining.

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
  • Note: A Strategy will fail when consumed by Pandas TA if there is no {"kind": "indicator name"} attribute. Remember to check your spelling.

Optional Parameters

  • 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.

Types of Strategies

Builtin

# Running the Builtin CommonStrategy as mentioned above
df.ta.strategy(ta.CommonStrategy)

# The Default Strategy is the ta.AllStrategy. The following are equivalent:
df.ta.strategy()
df.ta.strategy("All")
df.ta.strategy(ta.AllStrategy)

Categorical

# List of indicator categories
df.ta.categories

# Running a Categorical Strategy only requires the Category name
df.ta.strategy("Momentum") # Default values for all Momentum indicators
df.ta.strategy("overlap", length=42) # Override all Overlap 'length' attributes

Custom

# Create your own Custom Strategy
CustomStrategy = ta.Strategy(
    name="Momo and Volatility",
    description="SMA 50,200, BBANDS, RSI, MACD and Volume SMA 20",
    ta=[
        {"kind": "sma", "length": 50},
        {"kind": "sma", "length": 200},
        {"kind": "bbands", "length": 20},
        {"kind": "rsi"},
        {"kind": "macd", "fast": 8, "slow": 21},
        {"kind": "sma", "close": "volume", "length": 20, "prefix": "VOLUME"},
    ]
)
# To run your "Custom Strategy"
df.ta.strategy(CustomStrategy)

Multiprocessing

The Pandas TA strategy method utilizes multiprocessing for bulk indicator processing of all Strategy types with ONE EXCEPTION! When using the col_names parameter to rename resultant column(s), the indicators in ta array will be ran in order.

# VWAP requires the DataFrame index to be a DatetimeIndex.
# * Replace "datetime" with the appropriate column from your DataFrame
df.set_index(pd.DatetimeIndex(df["datetime"]), inplace=True)

# Runs and appends all indicators to the current DataFrame by default
# The resultant DataFrame will be large.
df.ta.strategy()
# Or the string "all"
df.ta.strategy("all")
# Or the ta.AllStrategy
df.ta.strategy(ta.AllStrategy)

# Use verbose if you want to make sure it is running.
df.ta.strategy(verbose=True)

# Use timed if you want to see how long it takes to run.
df.ta.strategy(timed=True)

# Choose the number of cores to use. Default is all available cores.
# For no multiprocessing, set this value to 0.
df.ta.cores = 4

# Maybe you do not want certain indicators.
# Just exclude (a list of) them.
df.ta.strategy(exclude=["bop", "mom", "percent_return", "wcp", "pvi"], verbose=True)

# Perhaps you want to use different values for indicators.
# This will run ALL indicators that have fast or slow as parameters.
# Check your results and exclude as necessary.
df.ta.strategy(fast=10, slow=50, verbose=True)

# Sanity check. Make sure all the columns are there
df.columns

Custom Strategy without Multiprocessing

Remember These will not be utilizing multiprocessing

NonMPStrategy = ta.Strategy(
    name="EMAs, BBs, and MACD",
    description="Non Multiprocessing Strategy by rename Columns",
    ta=[
        {"kind": "ema", "length": 8},
        {"kind": "ema", "length": 21},
        {"kind": "bbands", "length": 20, "col_names": ("BBL", "BBM", "BBU")},
        {"kind": "macd", "fast": 8, "slow": 21, "col_names": ("MACD", "MACD_H", "MACD_S")}
    ]
)
# Run it
df.ta.strategy(NonMPStrategy)

DataFrame Properties

adjusted

# Set ta to default to an adjusted column, 'adj_close', overriding default 'close'.
df.ta.adjusted = "adj_close"
df.ta.sma(length=10, append=True)

# To reset back to 'close', set adjusted back to None.
df.ta.adjusted = None

categories

# List of Pandas TA categories.
df.ta.categories

cores

# Set the number of cores to use for strategy multiprocessing
# Defaults to the number of cpus you have.
df.ta.cores = 4

# Set the number of cores to 0 for no multiprocessing.
df.ta.cores = 0

# Returns the number of cores you set or your default number of cpus.
df.ta.cores

datetime_ordered

# The 'datetime_ordered' property returns True if the DataFrame
# index is of Pandas datetime64 and df.index[0] < df.index[-1].
# Otherwise it returns False.
df.ta.datetime_ordered

exchange

# Sets the Exchange to use when calculating the last_run property. Default: "NYSE"
df.ta.exchange

# Set the Exchange to use.
# Available Exchanges: "ASX", "BMF", "DIFX", "FWB", "HKE", "JSE", "LSE", "NSE", "NYSE", "NZSX", "RTS", "SGX", "SSE", "TSE", "TSX"
df.ta.exchange = "LSE"

last_run

# Returns the time Pandas TA was last run as a string.
df.ta.last_run

reverse

# The 'reverse' is a helper property that returns the DataFrame
# in reverse order.
df.ta.reverse

prefix & suffix

# Applying a prefix to the name of an indicator.
prehl2 = df.ta.hl2(prefix="pre")
print(prehl2.name)  # "pre_HL2"

# Applying a suffix to the name of an indicator.
endhl2 = df.ta.hl2(suffix="post")
print(endhl2.name)  # "HL2_post"

# Applying a prefix and suffix to the name of an indicator.
bothhl2 = df.ta.hl2(prefix="pre", suffix="post")
print(bothhl2.name)  # "pre_HL2_post"

time_range

# Returns the time range of the DataFrame as a float.
# By default, it returns the time in "years"
df.ta.time_range

# Available time_ranges include: "years", "months", "weeks", "days", "hours", "minutes". "seconds"
df.ta.time_range = "days"
df.ta.time_range # prints DataFrame time in "days" as float

to_utc

# Sets the DataFrame index to UTC format.
df.ta.to_utc

DataFrame Methods

constants

import numpy as np

# Add constant '1' to the DataFrame
df.ta.constants(True, [1])
# Remove constant '1' to the DataFrame
df.ta.constants(False, [1])

# Adding constants for charting
import numpy as np
chart_lines = np.append(np.arange(-4, 5, 1), np.arange(-100, 110, 10))
df.ta.constants(True, chart_lines)
# Removing some constants from the DataFrame
df.ta.constants(False, np.array([-60, -40, 40, 60]))

indicators

# Prints the indicators and utility functions
df.ta.indicators()

# Returns a list of indicators and utility functions
ind_list = df.ta.indicators(as_list=True)

# Prints the indicators and utility functions that are not in the excluded list
df.ta.indicators(exclude=["cg", "pgo", "ui"])
# Returns a list of the indicators and utility functions that are not in the excluded list
smaller_list = df.ta.indicators(exclude=["cg", "pgo", "ui"], as_list=True)

ticker

# Download Chart history using yfinance. (pip install yfinance) https://github.com/ranaroussi/yfinance
# It uses the same keyword arguments as yfinance (excluding start and end)
df = df.ta.ticker("aapl") # Default ticker is "SPY"

# Period is used instead of start/end
# Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
# Default: "max"
df = df.ta.ticker("aapl", period="1y") # Gets this past year

# History by Interval by interval (including intraday if period < 60 days)
# Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
# Default: "1d"
df = df.ta.ticker("aapl", period="1y", interval="1wk") # Gets this past year in weeks
df = df.ta.ticker("aapl", period="1mo", interval="1h") # Gets this past month in hours

# BUT WAIT!! THERE'S MORE!!
help(ta.yf)

Indicators (by Category)

Candles (3)

  • Doji: cdl_doji
  • Inside Bar: cdl_inside
  • Heikin-Ashi: ha

Cycles (1)

  • Even Better Sinewave: ebsw

Momentum (37)

  • Awesome Oscillator: ao
  • Absolute Price Oscillator: apo
  • Bias: bias
  • Balance of Power: bop
  • BRAR: brar
  • Commodity Channel Index: cci
  • Chande Forecast Oscillator: cfo
  • Center of Gravity: cg
  • Chande Momentum Oscillator: cmo
  • Coppock Curve: coppock
  • Efficiency Ratio: er
  • Elder Ray Index: eri
  • Fisher Transform: fisher
  • Inertia: inertia
  • KDJ: kdj
  • KST Oscillator: kst
  • Moving Average Convergence Divergence: macd
  • Momentum: mom
  • Pretty Good Oscillator: pgo
  • Percentage Price Oscillator: ppo
  • Psychological Line: psl
  • Percentage Volume Oscillator: pvo
  • Quantitative Qualitative Estimation: qqe
  • Rate of Change: roc
  • Relative Strength Index: rsi
  • Relative Strength Xtra: rsx
  • Relative Vigor Index: rvgi
  • Slope: slope
  • SMI Ergodic smi
  • Squeeze: squeeze
    • Default is John Carter's. Enable Lazybear's with lazybear=True
  • Stochastic Oscillator: stoch
  • Stochastic RSI: stochrsi
  • TD Sequential: td_seq
    • Excluded from df.ta.strategy().
  • Trix: trix
  • True strength index: tsi
  • Ultimate Oscillator: uo
  • Williams %R: willr
Moving Average Convergence Divergence

(MACD)

Overlap (31)

  • Arnaud Legoux Moving Average: alma
  • Double Exponential Moving Average: dema
  • Exponential Moving Average: ema
  • Fibonacci's Weighted Moving Average: fwma
  • Gann High-Low Activator: hilo
  • High-Low Average: hl2
  • High-Low-Close Average: hlc3
    • Commonly known as 'Typical Price' in Technical Analysis literature
  • Hull Exponential Moving Average: hma
  • Holt-Winter Moving Average: hwma
  • Ichimoku Kinkō Hyō: ichimoku
    • Use: help(ta.ichimoku). Returns two DataFrames.
    • Drop the Chikou Span Column, the final column of the first resultant DataFrame, remove potential data leak.
  • Kaufman's Adaptive Moving Average: kama
  • Linear Regression: linreg
  • McGinley Dynamic: mcgd
  • Midpoint: midpoint
  • Midprice: midprice
  • Open-High-Low-Close Average: ohlc4
  • Pascal's Weighted Moving Average: pwma
  • WildeR's Moving Average: rma
  • Sine Weighted Moving Average: sinwma
  • Simple Moving Average: sma
  • Ehler's Super Smoother Filter: ssf
  • Supertrend: supertrend
  • Symmetric Weighted Moving Average: swma
  • T3 Moving Average: t3
  • Triple Exponential Moving Average: tema
  • Triangular Moving Average: trima
  • Variable Index Dynamic Average: vidya
  • Volume Weighted Average Price: vwap
    • Requires the DataFrame index to be a DatetimeIndex
  • Volume Weighted Moving Average: vwma
  • Weighted Closing Price: wcp
  • Weighted Moving Average: wma
  • Zero Lag Moving Average: zlma
Simple Moving Averages

(SMA) and Bollinger Bands (BBANDS)

Performance (4)

Use parameter: cumulative=True for cumulative results.

  • Draw Down: drawdown
  • Log Return: log_return
  • Percent Return: percent_return
  • Trend Return: trend_return
Percent Return

(Cumulative) with Simple Moving Average (SMA)

Statistics (9)

  • Entropy: entropy
  • Kurtosis: kurtosis
  • Mean Absolute Deviation: mad
  • Median: median
  • Quantile: quantile
  • Skew: skew
  • Standard Deviation: stdev
  • Variance: variance
  • Z Score: zscore
Z Score

Trend (15)

  • Average Directional Movement Index: adx
    • Also includes dmp and dmn in the resultant DataFrame.
  • Archer Moving Averages Trends: amat
  • Aroon & Aroon Oscillator: aroon
  • Choppiness Index: chop
  • Chande Kroll Stop: cksp
  • Decay: decay
    • Formally: linear_decay
  • Decreasing: decreasing
  • Detrended Price Oscillator: dpo
    • Set centered=False to remove potential data leak.
  • Increasing: increasing
  • Long Run: long_run
  • Parabolic Stop and Reverse: psar
  • Q Stick: qstick
  • Short Run: short_run
  • TTM Trend: ttm_trend
  • Vortex: vortex
Average Directional Movement Index

(ADX)

Utility (5)

  • Above: above
  • Above Value: above_value
  • Below: below
  • Below Value: below_value
  • Cross: cross

Volatility (13)

  • Aberration: aberration
  • Acceleration Bands: accbands
  • Average True Range: atr
  • Bollinger Bands: bbands
  • Donchian Channel: donchian
  • Keltner Channel: kc
  • Mass Index: massi
  • Normalized Average True Range: natr
  • Price Distance: pdist
  • Relative Volatility Index: rvi
  • Elder's Thermometer: thermo
  • True Range: true_range
  • Ulcer Index: ui
Average True Range

(ATR)

Volume (14)

  • Accumulation/Distribution Index: ad
  • Accumulation/Distribution Oscillator: adosc
  • Archer On-Balance Volume: aobv
  • Chaikin Money Flow: cmf
  • Elder's Force Index: efi
  • Ease of Movement: eom
  • Money Flow Index: mfi
  • Negative Volume Index: nvi
  • On-Balance Volume: obv
  • Positive Volume Index: pvi
  • Price-Volume: pvol
  • Price Volume Rank: pvr
  • Price Volume Trend: pvt
  • Volume Profile: vp
On-Balance Volume

(OBV)

Performance Metrics   BETA

Performance Metrics are a new addition to the package and consequentially are likely unreliable. Use at your own risk. These metrics return a float and are not part of the DataFrame Extension. They are called the Standard way. For Example:

import pandas_ta as ta
result = ta.cagr(df.close)

Available Metrics

  • Compounded Annual Growth Rate: cagr
  • Calmar Ratio: calmar_ratio
  • Downside Deviation: downside_deviation
  • Jensen's Alpha: jensens_alpha
  • Log Max Drawdown: log_max_drawdown
  • Max Drawdown: max_drawdown
  • Pure Profit Score: pure_profit_score
  • Sharpe Ratio: sharpe_ratio
  • Sortino Ratio: sortino_ratio
  • Volatility: volatility

Changes

General

  • A Strategy Class to help name and group your favorite indicators.
  • Some indicators have had their mamode kwarg updated with more moving average choices with the Moving Average Utility function ta.ma(). For simplicity, all choices are single source moving averages. This is primarily an internal utility used by indicators that have a mamode kwarg. This includes indicators: accbands, amat, aobv, atr, bbands, bias, efi, hilo, kc, natr, qqe, rvi, and thermo; the default mamode parameters have not changed. However, ta.ma() can be used by the user as well if needed. For more information: help(ta.ma)
    • Moving Average Choices: dema, ema, fwma, hma, linreg, midpoint, pwma, rma, sinwma, sma, swma, t3, tema, trima, vidya, wma, zlma.
  • An experimental and independent Watchlist Class located in the Examples Directory that can be used in conjunction with the new Strategy Class.
  • Linear Regression (linear_regression) is a new utility method for Simple Linear Regression using Numpy or Scikit Learn's implementation.
  • Added utility/convience function, to_utc, to convert the DataFrame index to UTC. See: help(ta.to_utc) Now as a Pandas TA DataFrame Property to easily convert the DataFrame index to UTC.

Breaking Indicators

  • Trend Return (trend_return) when given a trend Series like close > sma(close, 50) it now returns by default log and cumulative log returns of the trend as well as the Trends, Trades, Trade Entries and Trade Exits of that trend. Now compatible with vectorbt by setting asbool=True to get boolean Trade Entries and Exits. See: help(ta.trend_return)

New Indicators

  • Arnaud Legoux Moving Average (alma) uses the curve of the Normal (Gauss) distribution to allow regulating the smoothness and high sensitivity of the indicator. See: help(ta.alma) trading account, or fund. See: help(ta.drawdown)
  • Even Better Sinewave (ebsw) measures market cycles and uses a low pass filter to remove noise. See: help(ta.ebsw)
  • Tom DeMark's Sequential (td_seq) attempts to identify a price point where an uptrend or a downtrend exhausts itself and reverses. Currently exlcuded from df.ta.strategy() for performance reasons. See: help(ta.td_seq)

Updated Indicators

  • ADX (adx): Added mamode with default "RMA" and with the same mamode options as TradingView. See help(ta.adx).
  • Average True Range (atr): The default mamode is now "RMA" and with the same mamode options as TradingView. See help(ta.atr).
  • Bollinger Bands (bbands): New argument ddoff to control the Degrees of Freedom. Default is 0. See help(ta.bbands).
  • Chande Kroll Stop (cksp): Added tvmode with default True. When tvmode=False, cksp implements “The New Technical Trader” with default values. See help(ta.cksp).
  • Decreasing (decreasing): New argument strict checks if the series is continuously decreasing over period length. Default: False. See help(ta.decreasing).
  • Increasing (increasing): New argument strict checks if the series is continuously increasing over period length. Default: False. See help(ta.increasing).
  • Volume Weighted Average Price (vwap) Added a new parameter called anchor. Default: "D" for "Daily". See Timeseries Offset Aliases for additional options. Requires the DataFrame index to be a DatetimeIndex

Sources

Original TA-LIB | TradingView | Sierra Chart | MQL5 | FM Labs | Pro Real Code | User 42


About Joyk


Aggregate valuable and interesting links.
Joyk means Joy of geeK