Metadata-Version: 2.1
Name: techsig
Version: 0.1.0
Summary: Technical charts with signals
Home-page: UNKNOWN
Author: Aayush Talekar Saloni Jaitly
Author-email: aayush.talekar57@nmims.edu.in
License: UNKNOWN
Description: 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
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
