techsig 0.0.4

Technical charts with signals

TechSig

*Package to get technical indicators for given market data based on which Bull and Bear signals are generated. *This enables a non finance background person get the insights of the stock market technicalities in an understandable language. *Function to get the market data is also provided. *Plot are provided for all the techncial indicators which can help analyse the data better. ##Note- All investments, financial opinions expressed by techsig are from personal research and experience of the authors and are intended as educational material.

Author-

• Aayush Talekar
• Saloni Jaitly

Requirements:

*Pandas *Numpy *yfinance *plotly

Function description

get_data(ticker, start_date, end_date):

Import daily market data :param ticker: ticker name according to National Stock Exchange :param start_date: format 'yyyy-mm-dd' :param end_date: format 'yyyy-mm-dd' :return: pandas.DataFrame() : OHCLV data on a daily frequency

moving_average(df, exponential=False, simple=False, plot=False, signal=False):

Calculate simple and exponential moving average (ma) for given data :param df: pandas.DataFrame() :market data downloaded from get_data() :param exponential: Boolean: if True, exponential ma is displayed :param simple: Boolean: if True, simple ma is displayed :param plot: Boolean: if True, closing price with ma is plotted :param signal: Boolean: if True, bullish/bearish signals are returned :return: pandas.DataFrame() : moving average of 5 days, 10 days, 20 days, 50 days, 100 days and 200 days

MACD(df, a=12, b=26, c=9, signal=False, plot=False):

Calculate moving average convergence divergence (MACD) for given data
:param df: pandas.DataFrame() :market data downloaded from get_data()
:param a: number of periods for moving average fast line: default = 12
:param b: number of periods for moving average slow line: default = 26
:param c: number of periods for macd signal line: default = 9
:param plot: Boolean: if True, closing price with MACD is plotted
:param signal: Boolean: if True, bullish/bearish signals are returned
:return: pandas.DataFrame() : MA_Fast, MA_Slow, MACD, Signal and Positions are returned

RSI (df, time_window=14, signal=False, plot=False):

Calculate relative strength index (RSI) for given data
:param df: pandas.DataFrame() :market data downloaded from get_data()
:param time_window: number of periods for RSI : default = 14
:param plot: Boolean: if True, closing price with RSI is plotted
:param signal: Boolean: if True, bullish/bearish signals are returned
:return: pandas.DataFrame() : RSI and Position is returned

IchimokuCloud(df, plot=False):

Calculate Ichimoku Clouds for given data :param df: pandas.DataFrame() :market data downloaded from get_data() :param plot: Boolean: if True, closing price with Ichimoku Clouds are plotted :return: pandas.DataFrame(): Conv_line, Base_line, Lead_span_A, Lead_span_B and Lagging span

ADX(df, trend=False, plot=False):

Calculate average directional index for given data :param df: pandas.DataFrame() :market data downloaded from get_data() :param trend: Boolean: if True, strength of the trend is returned :param plot: Boolean: if True, closing price with ADX is plotted :return: pandas.DataFrame(): ADX, Positive Directional Index and Negative Directional Index

ATR(DF,n=14, plot=False):

Calculate average true range (ATR) for given data
:param DF: pandas.DataFrame() :market data downloaded from get_data()
:param n: number of periods for ATR: default = 14
:param plot: Boolean: if True, closing price with ATR is plotted
:return: pandas.DataFrame(): ATR 

stochastic_oscillator(df, signal=False, plot=False):

Calculate stochastic oscillator %K and %D for given data.    
:param df: pandas.DataFrame() :market data downloaded from get_data()
:param plot: Boolean: if True, closing price with stochastic oscillator is plotted
:param signal: Boolean: if True, bullish/bearish signals are returned
:return: pandas.DataFrame(): %K and %D values

OBV(DF, plot=False, signal=False):

Calculate on balance volume (OBV) for given data
:param DF: pandas.DataFrame() :market data downloaded from get_data()
:param plot: Boolean: if True, closing price with OBV is plotted
:param signal: Boolean: if True, bullish/bearish signals are returned
:return: pandas.DataFrame(): %K and %D values

ppsr(df):

Calculate Pivot Points, Supports and Resistances for given data
:param df: pandas.DataFrame() :market data downloaded from get_data()
:return: pandas.DataFrame() : Pivot Points, Resistances and Supports

semideviation(df):

Calculate semi deviation for given close price
:param df: pandas.DataFrame(): close price of data
:return: float: value of semi deviation

meandeviation(df):

Calculate mean deviation for given close price
:param df: pandas.DataFrame(): close price of data
:return: float: value of mean deviation

standard_deviation(df, n=21):

Calculate standard Deviation for given data.
:param df: pandas.DataFrame(): close price of data
:param n: number of periods: default = 21
:return: pandas.DataFrame(): moving standard deviations

TSI(df, r=25, s=13, c=9, signal=False, plot=False):

Calculate True Strength Index (TSI) for given data.
:param df: pandas.DataFrame(): market data downloaded from get_data()
:param r: time period for EMA_Fast: default = 25 
:param s: time period for EMA_SLow: default = 13
:param c: time period for Signal Line: default = 9
:param plot: Boolean: if True, closing price with TSI is plotted
:param signal: Boolean: if True, bullish/bearish signals are returned
:return: pandas.DataFrame(): Price Change(pc), Price Change Smoothed(pcs), Price Change Double Smooth(pcds), Absolute Price Change(apc),
Absolute Price Change Smoothed(apcs), Absolute Price Change Double Smooth(apcds), TSI and Signal

MFI(df, n=14, signal = False, plot=False):

Calculate Money Flow Index(MFI) for given data.
:param df: pandas.DataFrame(): market data downloaded from get_data()
:param n: number of periods for MFI: default = 14
:param plot: Boolean: if True, closing price with MFI is plotted
:param signal: Boolean: if True, bullish/bearish signals are returned
:return: pandas.DataFrame(): Typical Price, Money Flow, MFI

summ(data):

Calculate the summary of the latest date :param df: pandas.DataFrame(): market data downloaded from get_data() :return: pandas.DataFrame(): Three dataframes are returned viz. Moving Average, Technical Indicators and Pivot Points

sentiment_signal(data):

Analysing the overall sentiment based on techncial indicators :param df: pandas.DataFrame(): market data downloaded from get_data() :return: pandas.DataFrame(): bull/bear/neutral signal of the technical indicator