So I have an excel file containing data on a specific stock.
My excel file contains about 2 months of data, it monitors the Open price, Close price, High Price, Low Price and Volume of trades in 5 minute intervals, so there are about 3000 rows in my file.
I want to calculate the RSI (or EMA if it's easier) of a stock daily, I'm making a summary table that collects the daily data so it converts my table of 3000+ rows into a table with only about 60 rows (each row represents one day).
Essentially I want some sort of code that sorts the excel data by date then calculates the RSI as a single value for that day. RSI is given by: 100-(100/(1+RS)) where RS = average gain of up periods/average loss of down periods.
Note: My excel uses 'Datetime' so each row's 'Datetime' looks something like '2022-03-03 9:30-5:00' and the next row would be '2022-03-03 9:35-5:00', etc. So the code needs to just look at the date and ignore the time I guess.
Some code to maybe help understand what I'm looking for:
So here I'm calling my excel file, I want the code to take the called excel file, group data by date and then calculate the RSI of each day using the formula I wrote above.
dat = pd.read_csv('AMD_5m.csv',index_col='Datetime',parse_dates=['Datetime'],
date_parser=lambda x: pd.to_datetime(x, utc=True))
dates = backtest.get_dates(dat.index)
#create a summary table
cols = ['Num. Obs.', 'Num. Trade', 'PnL', 'Win. Ratio','RSI'] #add addtional fields if necessary
summary_table = pd.DataFrame(index = dates, columns=cols)
# loop backtest by dates
This is the code I used to fill out the other columns in my summary table, I'll put my SMA (simple moving average) function below.
for d in dates:
this_dat = dat.loc[dat.index.date==d]
#find the number of observations in date d
summary_table.loc[d]['Num. Obs.'] = this_dat.shape[0]
#get trading (i.e. position holding) signals
signals = backtest.SMA(this_dat['Close'].values, window=10)
#find the number of trades in date d
summary_table.loc[d]['Num. Trade'] = np.sum(np.diff(signals)==1)
#find PnLs for 100 shares
shares = 100
PnL = -shares*np.sum(this_dat['Close'].values[1:]*np.diff(signals))
if np.sum(np.diff(signals))>0:
#close position at market close
PnL += shares*this_dat['Close'].values[-1]
summary_table.loc[d]['PnL'] = PnL
#find the win ratio
ind_in = np.where(np.diff(signals)==1)[0]+1
ind_out = np.where(np.diff(signals)==-1)[0]+1
num_win = np.sum((this_dat['Close'].values[ind_out]-this_dat['Close'].values[ind_in])>0)
if summary_table.loc[d]['Num. Trade']!=0:
summary_table.loc[d]['Win. Ratio'] = 1. *num_win/summary_table.loc[d]['Num. Trade']
This is my function for calculating Simple Moving Average. I was told to try and adapt this for RSI or for EMA (Exponential Moving Average). Apparently adapting this for EMA isn't too troublesome but I can't figure it out.
def SMA(p,window=10,signal_type='buy only'):
#input price "p", look-back window "window",
#signal type = buy only (default) --gives long signals, sell only --gives sell signals, both --gives both long and short signals
#return a list of signals = 1 for long position and -1 for short position
signals = np.zeros(len(p))
if len(p)<window:
#no signal if no sufficient data
return signals
sma = list(np.zeros(window)+np.nan) #the first few prices does not give technical indicator values
sma += [np.average(p[k:k+window]) for k in np.arange(len(p)-window)]
for i in np.arange(len(p)-1):
if np.isnan(sma[i]):
continue #skip the open market time window
if sma[i]<p[i] and (signal_type=='buy only' or signal_type=='both'):
signals[i] = 1
elif sma[i]>p[i] and (signal_type=='sell only' or signal_type=='both'):
signals[i] = -1
return signals