cuansignal: Signal Indicator to Buy / Sell Stock
Bismillaahirrahmaanirrahiim. cuansignal is a library that contains several functions to predict and provide signals to buy and sell stocks. There are several indicators included in this library. In the initial version, there are four functions, namely Double Exponential Moving Average, Bollinger Band, Stochastics and Relative Strength Index. More functions will be added in the next edition. We welcome input and critics.
Function
Double Exponential Moving Average (dEMA)
cuansignal.dEMA(df, base, short, long)
df = data to be analyzed, with format ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']
base = parameter on which the EMA calculation is based, for example 'Close', or 'Adj Close', or something else
short = the first EMA period used in the model (which is shorter)
long = the second EMA period used in the model (which is longer)
The resulting output is a DataFrame>>> import yfinance as yf
>>> from cuansignal import signals as cs
>>> data = yf.download('AAPL', start='2018-01-01', end='2020-08-01')
>>> result = cs.dEMA(data, base='Close', short=10, long=100)
>>> print(result)
# Open High Low Close Adj Close Volume EMA 10 EMA 100 Buy Sell
# Date
# 2018-01-02 42.540001 43.075001 42.314999 43.064999 41.248272 102223600 43.064999 43.064999 NaN NaN
# 2018-01-03 43.132500 43.637501 42.990002 43.057499 41.241089 118071600 43.063635 43.064850 NaN 43.057499
# 2018-01-04 43.134998 43.367500 43.020000 43.257500 41.432659 89738400 43.098883 43.068665 43.2575 NaN
# 2018-01-05 43.360001 43.842499 43.262501 43.750000 41.904385 94640000 43.217268 43.082157 NaN NaN
# 2018-01-08 43.587502 43.902500 43.482498 43.587502 41.748737 82271200 43.284583 43.092164 NaN NaN
# ... ... ... ... ... ... ... ... ... ... ...
# 2020-07-27 93.709999 94.904999 93.480003 94.809998 94.034538 121214000 95.069989 82.520419 NaN NaN
# 2020-07-28 94.367500 94.550003 93.247498 93.252502 92.489784 103625600 94.739537 82.732936 NaN NaN
# 2020-07-29 93.750000 95.230003 93.712502 95.040001 94.262657 90329200 94.794167 82.976640 NaN NaN
# 2020-07-30 94.187500 96.297501 93.767502 96.190002 95.403267 158130000 95.047955 83.238291 NaN NaN
# 2020-07-31 102.885002 106.415001 100.824997 106.260002 105.390907 374336800 97.086509 83.694166 NaN NaNBollinger Band (bband)
cuansignal.bband(df, base, period, std)
df = data to be analyzed, with format ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']
base = parameter that is the basis for calculating Simple MA, for example 'Close', or 'Adj Close', or others
period = length of the period to be used in calculating the value of Simple MA and Standard Deviation
std = standard deviation multiplier
The resulting output is a DataFrame>>> import yfinance as yf
>>> from cuansignal import signals as cs
>>> data = yf.download('AAPL', start='2018-01-01', end='2020-08-01')
>>> result = cs.bband(data, base='Close', period=30, std=2)
>>> print(result)
# Open High Low Close Adj Close Volume SMA STD Upper Lower Buy Sell
# Date
# 2018-01-02 42.540001 43.075001 42.314999 43.064999 41.248272 102223600 NaN NaN NaN NaN NaN NaN
# 2018-01-03 43.132500 43.637501 42.990002 43.057499 41.241089 118071600 NaN NaN NaN NaN NaN NaN
# 2018-01-04 43.134998 43.367500 43.020000 43.257500 41.432659 89738400 NaN NaN NaN NaN NaN NaN
# 2018-01-05 43.360001 43.842499 43.262501 43.750000 41.904385 94640000 NaN NaN NaN NaN NaN NaN
# 2018-01-08 43.587502 43.902500 43.482498 43.587502 41.748737 82271200 NaN NaN NaN NaN NaN NaN
# ... ... ... ... ... ... ... ... ... ... ... ... ...
# 2020-07-27 93.709999 94.904999 93.480003 94.809998 94.034538 121214000 92.712333 3.608910 99.930152 85.494514 NaN NaN
# 2020-07-28 94.367500 94.550003 93.247498 93.252502 92.489784 103625600 92.962499 3.361075 99.684650 86.240349 NaN NaN
# 2020-07-29 93.750000 95.230003 93.712502 95.040001 94.262657 90329200 93.196500 3.247561 99.691622 86.701377 NaN NaN
# 2020-07-30 94.187500 96.297501 93.767502 96.190002 95.403267 158130000 93.472916 3.131830 99.736577 87.209256 NaN NaN
# 2020-07-31 102.885002 106.415001 100.824997 106.260002 105.390907 374336800 94.083833 3.741930 101.567694 86.599972 NaN 106.260002Relative Strength Index
cuansignal.rsi(df, base, EMA, MA, RSI)
df = data to be analyzed, with format ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']
base = parameter on which the RSI calculation is based, for example 'Close', or 'Adj Close', or something else
EMA = the length of the period to be used in calculating the EMA value for the Average Up and Down periods
MA = the length of the MA period which is the cut-of buying signal
RSI = RSI value which is the reference for buying signal
The resulting output is a DataFrame>>> import yfinance as yf
>>> from cuansignal import signals as cs
>>> data = yf.download('AAPL', start='2018-01-01', end='2020-08-01')
>>> result = cs.rsi(data, base='Close', EMA=11, MA=200, RSI=30)
>>> print(result)
# Open High Low Close Adj Close Volume MA200 ... Downmove Avg Up Avg Down RS RSI Buy Sell
# Date ...
# 2018-01-02 42.540001 43.075001 42.314999 43.064999 41.248272 102223600 NaN ... 0.000000 0.000000 0.000000 NaN NaN NaN NaN
# 2018-01-03 43.132500 43.637501 42.990002 43.057499 41.241089 118071600 NaN ... 0.000174 0.000000 0.000095 0.000000 0.000000 NaN NaN
# 2018-01-04 43.134998 43.367500 43.020000 43.257500 41.432659 89738400 NaN ... 0.000000 0.001838 0.000057 32.006998 96.970339 NaN NaN
# 2018-01-05 43.360001 43.842499 43.262501 43.750000 41.904385 94640000 NaN ... 0.000000 0.004911 0.000039 126.150084 99.213528 NaN NaN
# 2018-01-08 43.587502 43.902500 43.482498 43.587502 41.748737 82271200 NaN ... 0.003714 0.003543 0.001063 3.332459 76.918422 NaN NaN
# ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
# 2020-07-27 93.709999 94.904999 93.480003 94.809998 94.034538 121214000 74.389675 ... 0.000000 0.007620 0.007816 0.975028 49.367794 NaN NaN
# 2020-07-28 94.367500 94.550003 93.247498 93.252502 92.489784 103625600 74.568325 ... 0.016428 0.006350 0.009251 0.686457 40.704080 NaN NaN
# 2020-07-29 93.750000 95.230003 93.712502 95.040001 94.262657 90329200 74.748262 ... 0.000000 0.008487 0.007709 1.100866 52.400580 NaN NaN
# 2020-07-30 94.187500 96.297501 93.767502 96.190002 95.403267 158130000 74.934375 ... 0.000000 0.009089 0.006424 1.414785 58.588444 NaN NaN
# 2020-07-31 102.885002 106.415001 100.824997 106.260002 105.390907 374336800 75.171525 ... 0.000000 0.025022 0.005354 4.673949 82.375592 NaN NaN Stochastics
cuansignal.stoch(df, period, period2, high, low)
df = data to be analyzed, with format ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']
period = the length of the period to be used in calculating the value of %K
period2 = the length of the period to be used in calculating the value of %D
high = high limit value which becomes the cut-of buying/selling signal
low = low limit value which becomes the cut-of buying/selling signal
The resulting output is a DataFrame>>> import yfinance as yf
>>> from cuansignal import signals as cs
>>> data = yf.download('AAPL', start='2018-01-01', end='2020-08-01')
>>> result = cs.stoch(data, period=50, period2=3, high=70, low=30)
>>> print(result)
# Open High Low Close Adj Close Volume 50-high 50-low %K %D Buy Sell Stoch Signal
# Date
# 2018-01-02 42.540001 43.075001 42.314999 43.064999 41.248272 102223600 NaN NaN NaN NaN NaN NaN 0
# 2018-01-03 43.132500 43.637501 42.990002 43.057499 41.241089 118071600 NaN NaN NaN NaN NaN NaN 0
# 2018-01-04 43.134998 43.367500 43.020000 43.257500 41.432659 89738400 NaN NaN NaN NaN NaN NaN 0
# 2018-01-05 43.360001 43.842499 43.262501 43.750000 41.904385 94640000 NaN NaN NaN NaN NaN NaN 0
# 2018-01-08 43.587502 43.902500 43.482498 43.587502 41.748737 82271200 NaN NaN NaN NaN NaN NaN 0
# ... ... ... ... ... ... ... ... ... ... ... ... ... ...
# 2020-07-27 93.709999 94.904999 93.480003 94.809998 94.034538 121214000 99.955002 75.052498 79.339410 73.771033 NaN NaN 0
# 2020-07-28 94.367500 94.550003 93.247498 93.252502 92.489784 103625600 99.955002 77.580002 70.044695 73.303047 NaN NaN 0
# 2020-07-29 93.750000 95.230003 93.712502 95.040001 94.262657 90329200 99.955002 78.252502 77.352835 75.578980 NaN NaN 0
# 2020-07-30 94.187500 96.297501 93.767502 96.190002 95.403267 158130000 99.955002 78.272499 82.635771 76.677767 NaN NaN 0
# 2020-07-31 102.885002 106.415001 100.824997 106.260002 105.390907 374336800 106.415001 78.272499 99.449236 86.479281 NaN NaN 0Changelog
Version 1.1.2 (2021.08.19)
- Updating Minor Bugs
- Add Github Link
Version 1.1.1 (2021.08.19)
- Updating Minor Bugs
Version 1.1.0 (2021.08.19)
- Updating Minor Bugs
- Updating Readme.md
Version 1.0.0 (2021.08.19)
- Initial release
License
cuansignal is licensed under the MIT license.
