# Relative strength index (RSI)

### Relative Strength index (RSI)

It’s famouse for secondary indicator of stock’s market. This is presented by variance for changing upper and down.

It has tendency if it decrease, it is creasing and also if it crease, it is decresing.

Recently it should have been not true surely. However, it has been importnat index for advance afterwards.

### Class code

Anyway, this is presented how to calculate the RSI.

``````# classRSI.py

import numpy as np
import pandas as pd

class Stocks:
def __init__(self, symbol, start_day, end_day):
self.symbol = symbol
self.start_day = start_day
self.end_day = end_day

def calcRSI(self, period=14):
date_index = df.index.astype('str')
AU = pd.DataFrame(U, index=date_index).rolling(window=period, min_periods=1).mean()
RSI = AU/ (AD+AU) * 100

df.insert(len(df.columns), 'RSI', RSI)
df.insert(len(df.columns), 'RSI signal', df['RSI'].rolling(window=9, min_periods=1).mean())

return df

``````

RSI needs variance of upper and down with average of this. And It is calculated

RSI = Average of Upper / (Average of Upper + Average of Down) * 100

Caculating RSI signal which is calculated moving avereage for RSI, it’s comparing with two indexs.

### Main code of plotting

`````` # PlotRSI.py

import numpy as np
import pandas as pd
import datetime
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

from classRSI import Stocks

def main():
td_1y = datetime.timedelta(weeks=52/2)
today = datetime.datetime.now()
start_day = today - td_1y

symbol = input('Write ticker name like aapl: ') #^IXIC

stock = Stocks(symbol, start_day, today)

df= stock.calcRSI()

index = df.index.astype('str')

fig = plt.figure(figsize=(10,10))

ax_main = plt.subplot(1,1,1)

def x_date(x,pos):
try:
return index[int(x-0.5)][:7]
except IndexError:
return ''

# For figure
ax_main.xaxis.set_major_locator(ticker.MaxNLocator(10))
ax_main.xaxis.set_major_formatter(ticker.FuncFormatter(x_date))

ax_main.plot(index, df['RSI'], label='RSI')
ax_main.plot(index, df['RSI signal'], label='RSI signal')

plt.grid()
plt.show()

if __name__ == "__main__":
main()

``````

It is simply calculating for start day to today and plot

### Example figure Typical ticker is AAPL(Apple). This figure is presented at 2021-06-26.

you just know that this plot what is mean :)

### Model, Selection

For using RSI, we can make model and select ticker in index (Dow, S&P500 etc) ?

I have simple idea to use RSI value of down to up and up to down.

• It is true that RSI value is under 30?

In my case, I have choose selected data by Bollinger band’s model. This model is explained this post which is also important finance index.

`````` # modelRSI.py

import numpy as np
import pandas as pd
import datetime
from tqdm import tqdm

from classRSI import Stocks

def main(stock_list, day_init=datetime.datetime(2020,1,1), today=datetime.datetime.now()):
print(stock_list)

selected_ticker = []
for ticker in tqdm(stock_list):
stock = Stocks(ticker, start_day, today)
df = stock.calcRSI()
df_recent = df.iloc[-1:]
value_RSI = float(df_recent['RSI'])
value_RSI_signal = float(df_recent['RSI signal'])
down = 40
if value_RSI < down:
if value_RSI < value_RSI_signal:
selected_ticker.append(ticker)

print(selected_ticker)

df = pd.DataFrame(selected_ticker, columns=['Ticker'])
df.to_json(url)

if __name__ == '__main__':
td_1y = datetime.timedelta(weeks=52/2)
today = datetime.datetime.now()
start_day = today - td_1y

main(stock_list = s_list, day_init = start_day)
``````

If you change var of down like 30 to 40, you should get many ticker beyond hard condition.

### Conclusion

Let us know what is RSI and how to caclulate with figure. And then we are going to find ticker using this model :)

If you want to use this code, I’m very sorry that you should change code and make directory for data a little bit.

I should appreciate and refer for many blog on google. Thanks a lot.

If you satisfied this post you should check Github and please Star :)