In this article we will compute weekly RSI (Relative strength Index) for stocks from daily stock data. Data from the API is in daily format. For longer swing trades or simply for long term investing entrypoints it is convenient to look on the bigger picture. Hence we would like our indicators to give us signals on weekly timeframe. Source of the data will be Yahoo! Finance API.

Main steps that will be performed:

  • download the data from an API
  • aggregate daily data to weekly time window
  • compute RSI on the weekly data
  • write simple RSI screening function to give buy/sell signals on weekly time frame

Import libraries

In [238]:
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# ___library_import_statements___
import pandas as pd
# make pandas to print dataframes nicely
pd.set_option('expand_frame_repr', False)  
import as web
import matplotlib.pyplot as plt
import datetime
#newest yahoo API 
import yfinance as yahoo_finance
%matplotlib inline

Choose a ticker and data date range

In [239]:
# ___variables___
ticker = 'TSLA'                                               # Tesla stock

start_time = datetime.datetime(2017, 10, 1)
#end_time = datetime.datetime(2019, 1, 20)
end_time =         # today

Download the data from Yahoo! and put daily data into dataframe.

In [240]:
# yahoo gives only daily historical data
def get_data(ticker, start_time, end_time):
    connected = False
    while not connected:
            df = web.get_data_yahoo(ticker, start=start_time, end=end_time)
            connected = True
            print('connected to yahoo')
        except Exception as e:
            print("type error: " + str(e))
            time.sleep( 5 )
    return df    

# run the function
df = get_data(ticker, start_time, end_time)   
connected to yahoo
In [241]:
# optional printing
# print(df.head(7))
# print(df.tail(7))

Aggregate to weekly data

Resample daily data to weekly data and put it to new dataframe called r_df.

In [242]:
# resample with dictionary
# each column has to be handled differently
agg_dict = {'Open': 'first',
          'High': 'max',
          'Low': 'min',
          'Close': 'last',
          'Adj Close': 'last',
          'Volume': 'mean'}

r_df = df.resample('W').agg(agg_dict)

df is dataframe with daily data. r_df is resampled dataframe to weekly timeframe.

In [243]:
# optional printing
# print(df.head(15))
# print('---------')
# print(r_df.tail(7))

Visualize the data

Plot daily versus weekly price data.

In [244]:
def plot_OHLC(data, ticker, window):

    #to avoid matplotlib future warning
    from pandas.plotting import register_matplotlib_converters
    plt.title('{} price data to {} timeframe'.format(ticker, window))
    plt.plot(data['Adj Close'])
In [245]:
plot_OHLC(df, ticker, 'daily')
plot_OHLC(r_df, ticker, 'weekly')

Given a dataframe, compute RSI:

In [246]:
def computeRSI(data, time_window):
    diff = data.diff(1).dropna()        # diff in one field(one day)

    #this preservers dimensions off diff values
    up_chg = 0 * diff
    down_chg = 0 * diff
    # up change is equal to the positive difference, otherwise equal to zero
    up_chg[diff > 0] = diff[ diff>0 ]
    # down change is equal to negative deifference, otherwise equal to zero
    down_chg[diff < 0] = diff[ diff < 0 ]
    # check pandas documentation for ewm
    # values are related to exponential decay
    # we set com=time_window-1 so we get decay alpha=1/time_window
    up_chg_avg   = up_chg.ewm(com=time_window-1 , min_periods=time_window).mean()
    down_chg_avg = down_chg.ewm(com=time_window-1 , min_periods=time_window).mean()
    rs = abs(up_chg_avg/down_chg_avg)
    rsi = 100 - 100/(1+rs)
    return rsi

Add newly computed RSI column to our daily and weekly dataframes.

In [247]:
df['RSI'] = computeRSI(df['Adj Close'], 14)
r_df['RSI'] = computeRSI(r_df['Adj Close'], 14)
In [248]:
# optional printing
# print(df.head())
# print(df.tail())

Define RSI visualization function.

In [249]:
def plot_price_and_RSI(dataframe, ticker, window):

    # plot price
    plt.plot(dataframe['Adj Close'])
    plt.title('{} price data (Adj Close) to {} timeframe'.format(ticker, window))

    # plot corresponding RSI values and significant levels
    plt.title('RSI chart')
    plt.title('{} RSI chart to {} timeframe'.format(ticker, window))

    plt.axhline(0, linestyle='--', alpha=0.1)
    plt.axhline(20, linestyle='--', alpha=0.5)
    plt.axhline(30, linestyle='--')

    plt.axhline(70, linestyle='--')
    plt.axhline(80, linestyle='--', alpha=0.5)
    plt.axhline(100, linestyle='--', alpha=0.1)

Plot daily stock price and RSI

In [250]:
plot_price_and_RSI(df, ticker, 'daily')

Plot weekly stock price and RSI

The weeklygraphs are the ones we are most interested in.

In [251]:
plot_price_and_RSI(r_df, ticker, 'weekly')

Build simple stock buy/sell reccomendation system

Let's screen for some significant values of RSI that might give us actionable signals.

Now that we have weekly resampled RSI data in our dataframe, we can write super simple screener that would give us buy or sell signal based on the value of RSI.

  • weekly RSI is > 70 ... sell signal
  • weekly RSI is < 30 ... buy signal
In [252]:
print('buy signal for below dates and prices based on RSI value')
print(r_df.loc[r_df['RSI'] < 30])
buy signal for below dates and prices based on RSI value
                  Open   High         Low       Close   Adj Close    Volume        RSI
2019-06-02  191.199997  195.0  184.100006  185.160004  185.160004  10153675  29.718747
In [253]:
print('sell signal for below dates and prices based on RSI value')
print(r_df.loc[r_df['RSI'] > 70])
sell signal for below dates and prices based on RSI value
                  Open        High         Low       Close   Adj Close    Volume        RSI
2019-11-17  343.950012  356.329987  342.000000  352.170013  352.170013   7408020  70.414733
2019-12-22  362.549988  413.000000  362.500000  405.589996  405.589996  14730360  74.434420
2019-12-29  411.779999  435.309998  410.000000  430.380005  430.380005  10488475  76.934457
2020-01-05  428.790009  454.000000  402.079987  443.010010  443.010010  12545675  78.108997
2020-01-12  440.470001  498.799988  440.000000  478.529999  478.529999  18698713  81.034044

Rewrite screener into a function that will give us buy/sell signals:

In [254]:
# r_df['RSI'].iloc[-1] means last/current RSI value
# the RSI value is for "today"

def scan_RSI(data, ticker):
    print('Ticker:', ticker)
    if r_df['RSI'].iloc[-1] > 70:
        print('Current Weekly RSI:', r_df['RSI'].iloc[-1])
        print('potential short/sell signal')
    elif r_df['RSI'].iloc[-1] < 30:
        print('Current Weekly RSI:', r_df['RSI'].iloc[-1])
        print('potential long/buy signal')
        print('Current Weekly RSI:', r_df['RSI'].iloc[-1])
        print('no current weekly RSI signals')

Call the function to to get buy/sell signal for current price.

In [255]:
scan_RSI(r_df, ticker)
Ticker: TSLA
Current Weekly RSI: 81.03404442547773
potential short/sell signal

This function can be eventually part of stock sceening script.