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Pycharm printing an empty index that shouldn't be empty

When running this code, I am expecting a table to print from data_period, however pycharm is returning an empty index when it is run. My friend, using the same code on pycharm, gets the desired table. So we think it has to do with the files that pycharm is using/storing information in. Any other ideas?
import yfinance as yf
import pandas as pd
import numpy as np
from pandas_market_calendars import get_calendar
# Get the calendar for the NYSE
nyse = get_calendar('NYSE')
# Get the trading days for the NYSE between the start and end date
start_date = '2020-01-01'
end_date = '2023-01-26'
schedule = nyse.schedule(start_date, end_date)
trading_days = schedule.index
# Download the daily price action of the S&P 500
data = yf.download("SPY", start=start_date, end=end_date)
# Filter out days when the market is closed
data_period = data.loc[data.index.isin(trading_days)]
# Number of periods to calculate the standard deviation
averagingPeriod = 30
data_period['up'] = 100 * (data_period['High'].shift(1) - data_period['Open'].shift(1)) / data_period['Close'].shift(1)
data_period['down'] = 100 * np.abs(data_period['Open'].shift(1) - data_period['Low'].shift(1)) / data_period['Close'].shift(1)
# standard deviation
std_dev = data_period[['up','down']].rolling(window=averagingPeriod).std()
std_dev.rename(columns={'up':'up_std_dev','down':'down_std_dev'}, inplace=True)
# average
average = data_period[['up','down']].rolling(window=averagingPeriod).mean()
average.rename(columns={'up':'ave_up','down':'ave_down'}, inplace=True)
# H1 resistance level
data_period['H1'] = data_period['Open'] + (average['ave_up'] / 100) * data_period['Open']
# H2 resistance level
data_period['H2'] = data_period['Open'] + ((average['ave_up'] + std_dev['up_std_dev']) / 100) * data_period['Open']
# L1 support level
data_period['L1'] = data_period['Open'] - (average['ave_down'] / 100) * data_period['Open']
# L2 support level
data_period['L2'] = data_period['Open'] - ((average['ave_down'] + std_dev['down_std_dev']) / 100) * data_period['Open']
print(data_period)
I tried to get data_period to print out a full table, but I instead received an empty index.

efficient frontier/stock analyze

Consider the following task. Using a 10-year period I should calculate the portfolio weights in January and then use these weights in February to calculate the portfolio return and standard deviation. The program should then continue to calculate the weights In February and then use these weights in February to calculate the portfolio returns and standard deviation in marts. This should be done through all the 131 months in the data meaning I should only calculate the weights in the first month of the dataset.
import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import yfinance as yf
p_ret = [] # Define an empty array for portfolio returns
p_vol = [] # Define an empty array for portfolio volatility
tickers = ['AAPL', 'AMZN', 'XOM']
start_date = datetime.date(2010, 1, 2)
end_date = datetime.date(2020, 12, 31)
daily_data = yf.download(tickers, start=start_date, end=end_date) # definere datasættet
daily_data = daily_data['Adj Close'].dropna()
Vector_of_ones = np.array([1,1,1])
frames = [v for _, v in daily_data.groupby(pd.Grouper(freq='M'))]
rf = 0.01 # risk free asset
weights = []
df = pd.DataFrame(columns=tickers)
for w in frames:
#corr_matrix = w.pct_change().apply(lambda x: np.log(1 + x)).corr()
mu = (w.resample('D').last().pct_change().sum())
individual_asset_return = np.subtract(np.transpose(mu), np.dot(Vector_of_ones,rf))
# individual_asset_return = daily_data.pct_change().mean() # finder gennemsnittet
df.loc[+1] = [individual_asset_return[tickers[0]], individual_asset_return[tickers[1]],
individual_asset_return[tickers[2]]]
df.index = df.index - 1
df = df.sort_index()
for d in range(len(df)):
cov_matrix = w.pct_change().apply(lambda x: np.log(1 + x)).cov()
liste = df.iloc[d].tolist()
a = np.dot(np.linalg.inv(cov_matrix), np.transpose(np.array(liste)))
omega_weights = a / (np.dot(np.transpose(Vector_of_ones), a)) # expression to find weights
weights.append(omega_weights)
for afkast in frames[1:]: #loop to find the portfolio returns and standard deviation
cov_matrix1 = afkast.pct_change().apply(lambda x: np.log(1 + x)).cov()
#corr_matrix1 = afkast.pct_change().apply(lambda x: np.log(1 + x)).corr()
df1 = df.iloc[1:, :]
for d1 in range(len(df)):
liste1 = df.iloc[d1].tolist()
portfolio_return = np.dot(np.transpose(omega_weights),
mu)
p_ret.append(portfolio_return)
volatility_portfolio = np.sqrt(np.dot(np.transpose(omega_weights), np.dot(cov_matrix1, omega_weights)))
p_vol.append(volatility_portfolio)
data = {'Returns': p_ret, 'Volatility': p_vol}
for counter, symbol in enumerate(afkast.columns.tolist()):
# print(counter, symbol)
data[symbol + ' weight'] = [w[counter] for w in weights]
portfolios = pd.DataFrame(data) # laver dataframe som sortere sådan at den med mindst volatility er øverst
portfolios['Date'] = pd.date_range(start=start_date, periods=len(portfolios), freq='M')
portfolios.plot(x='Date', y='Returns', kind='line')
# portfolios.plot(x = 'Date', y = 'Volatility', kind = 'line')
plt.show()
print(portfolios.head())
As you probably can see I’m not an advanced coder but I hope I could some help where my code is wrong if there is anything wrong.
I really appreciate any help you can provide.

Python Pandas code takes over a minute to process the data

I have two issues with the below code:
(a) It takes over a minutes to process a record
(b) I receive a Warning [A value is trying to be set on a copy of a slice from a DataFrame. See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy]
for stock in stocklist:
...
...
df['Up Move'] = np.nan
df['Down Move'] = np.nan
df['Average Up'] = np.nan
df['Average Down'] = np.nan
df['RS'] = np.nan
df['RSI'] = np.nan
for x in range(1, len(df)):
df['Up Move'][x] = 0
df['Down Move'][x] = 0
if df['Close'][x] > df['Close'][x-1]:
df['Up Move'][x] = df['Close'][x] - df['Close'][x-1]
if df['Close'][x] < df['Close'][x-1]:
df['Down Move'][x] = abs(df['Close'][x] - df['Close'][x-1])
#Calculate initial Average Up & Down, RS and RSI
df['Average Up'][14] = df['Up Move'][1:15].mean()
df['Average Down'][14] = df['Down Move'][1:15].mean()
df['RS'][14] = df['Average Up'][14] / df['Average Down'][14]
df['RSI'][14] = 100 - (100/(1+df['RS'][14]))
#Calculate rest of Average Up, Average Down, RS, RSI
for x in range(15, len(df)):
df['Average Up'][x] = (df['Average Up'][x-1]*13+df['Up Move'][x])/14
df['Average Down'][x] = (df['Average Down'][x-1]*13+df['Down Move'][x])/14
df['RS'][x] = df['Average Up'][x] / df['Average Down'][x]
df['RSI'][x] = 100 - (100/(1+df['RS'][x]))
df.drop(['Up Move', 'Down Move', 'Average Up', 'Average Down'], axis = 1, inplace = True)

Try to avoid using for loop to handle dataframe.
the code:
#Calculate rest of Average Up, Average Down, RS, RSI
for x in range(15, len(df)):
df['Average Up'][x] = (df['Average Up'][x-1]*13+df['Up Move'][x])/14
df['Average Down'][x] = (df['Average Down'][x-1]*13+df['Down Move'][x])/14
df['RS'][x] = df['Average Up'][x] / df['Average Down'][x]
df['RSI'][x] = 100 - (100/(1+df['RS'][x]))
may equal to:
df.loc[15:, 'Average Up'] = df.loc[15:, 'Average Up'].shift(-1)*13 + df.loc[15:, 'Up Move'][x]/14
df.loc[15:, 'Average Down'] = df.loc[15:, 'Average Down'].shift(-1)*13 + df.loc[15:, 'Up Down'][x]/14
df.loc[15:, 'RS'] = df.loc[15:, 'Average Up']/ df.loc[15:, 'Average Down']
df.loc[15:, 'RSI'] = 100 - (100/(1+df.loc[15:, 'RS'][x]))

Just found the code that gives me the same right result in a couple of seconds. The earlier code would take about one minute to three minutes.
# Compute RSI
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
# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.ewm.html
# 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
# Calculate the RSI values using our function and add them to the dataframe.
df['RSI'] = computeRSI(df['Adj Close'], 14)
print(df.head())
print(df.tail())

Fetching date of high and low prices for week based on daily high low prices

First of all I will share objective of running python code.
Getting Daily High and Low Prices for a stock from Yahoo.
Converting the daily high and lows to Weekly High/Lows, monthly High Lows, Yearly High Lows.
Getting exact dates of Weekly or Monthly High Lows from a daily dataframe
Finally after fetching Dates for Weekly(or Monthly)High & lows, I want to arrange the data of what occured first High or Low during the week. for eg. during week ending 12th December, 2020, I get High of the week is 100 and low of week is 97(after completing step 2) and also High date and low date from daily dataframe (from step 3), I want to arrange Prices in order of occurence. so if High happened on 9th December and Low happened on 12th December. The prices will be arranged as 100 in row 1 and then 97 in row 2 and this process repeats for entire data frame.
What I have been able to achieve.
I have completed step 1 and step 2. Struggling in step for 3 as of now.
Have accomplished Step 1 by
import pandas as pd
import yfinance as yf
Ticker = '^NSEI'
f = yf.download(Ticker,period="max")
f = f.drop(['Adj Close'], axis=1)
f = f.drop(['Open'], axis=1)
f = f.drop(['Close'], axis=1)
f = f.drop(['Volume'], axis=1)
f.reset_index(inplace=True)
f.insert(0,'Ticker',Ticker)
Step 2 by
fw = f.groupby(['Ticker', pd.Grouper(key='Date', freq='W')])\
.agg(High=pd.NamedAgg(column='High', aggfunc='max'),
Low=pd.NamedAgg(column='Low', aggfunc='min'))\
.reset_index()
fm = f.groupby(['Ticker', pd.Grouper(key='Date', freq='M')])\
.agg(High=pd.NamedAgg(column='High', aggfunc='max'),
Low=pd.NamedAgg(column='Low', aggfunc='min'))\
.reset_index()
fq = f.groupby(['Ticker', pd.Grouper(key='Date', freq='Q')])\
.agg(High=pd.NamedAgg(column='High', aggfunc='max'),
Low=pd.NamedAgg(column='Low', aggfunc='min'))\
.reset_index()
fy = f.groupby(['Ticker', pd.Grouper(key='Date', freq='Y')])\
.agg(High=pd.NamedAgg(column='High', aggfunc='max'),
Low=pd.NamedAgg(column='Low', aggfunc='min'))\
.reset_index()
Struggling with step 3. used pd.merge, pd.join, pd.concat but unable to combine Weekly dataframe with dataframe on Highs and lows. The no of weekly records increase by performing merge and drop duplcates also didn't work properly when specified keep last.
So if you all can help me in step 3 and 4 would be grateful. Thanks

Solved the query which i posted above. Hope this help others. Thanks
import pandas as pd
import yfinance as yf
import datetime as dt
import numpy as np
Ticker = '^NSEI'
df = yf.download(Ticker, period='max')
df= df.drop(['Open', 'Close', 'Adj Close', 'Volume'], axis = 1).reset_index()
# Daily 3238 columns for reference
#Adding columns for weekly, monthly,6 month,Yearly,
df['WkEnd'] = df.Date.dt.to_period('W').apply(lambda r: r.start_time) + dt.timedelta(days=6)
df['MEnd'] = (df.Date.dt.to_period('M').apply(lambda r: r.end_time)).dt.date
df['6Mend'] = np.where(df.Date.dt.month <= 6,(df.Date.dt.year).astype(str)+'-1H',(df['Date'].dt.year).astype(str)+'-2H')
df['YEnd'] = (df.Date.dt.to_period('Y').apply(lambda r: r.end_time)).dt.date
# key variable for melting
d = {'Date':['Hidate', 'Lodate'], 'Price':['High','Low']}
#creating weekly neoformat
dw = df.groupby(['WkEnd']).agg({'High' : 'max','Low' : 'min' }).reset_index()
dw['Hidate'] = dw[['WkEnd','High']].merge(df,how = 'left').Date
dw['Lodate'] = dw[['WkEnd','Low']].merge(df,how = 'left').Date
dw = pd.lreshape(dw,d)
dw = dw.sort_values(by = ['Date']).reset_index()
dw = dw.drop(['index'], axis = 1)
#creating Monthly neoformat
dm = df.groupby(['MEnd']).agg({'High' : 'max','Low' : 'min' }).reset_index()
dm['Hidate'] = dm[['MEnd','High']].merge(df,how = 'left').Date
dm['Lodate'] = dm[['MEnd','Low']].merge(df,how = 'left').Date
dm = pd.lreshape(dm,d)
dm = dm.sort_values(by = ['Date']).reset_index()
dm = dm.drop(['index'], axis = 1)
#creating 6mth neoformat
d6m = df.groupby(['6Mend']).agg({'High' : 'max','Low' : 'min' }).reset_index()
d6m['Hidate'] = d6m[['6Mend','High']].merge(df,how = 'left').Date
d6m['Lodate'] = d6m[['6Mend','Low']].merge(df,how = 'left').Date
d6m = pd.lreshape(d6m,d)
d6m = d6m.sort_values(by = ['Date']).reset_index()
d6m = d6m.drop(['index'], axis = 1)
#creating Yearly neoformat
dy = df.groupby(['YEnd']).agg({'High' : 'max','Low' : 'min' }).reset_index()
dy['Hidate'] = dy[['YEnd','High']].merge(df,how = 'left').Date
dy['Lodate'] = dy[['YEnd','Low']].merge(df,how = 'left').Date
dy = pd.lreshape(dy,d)
dy = dy.sort_values(by = ['Date']).reset_index()
dy = dy.drop(['index'], axis = 1)

Use a pandas DataFrame created inside a function outside of the function

I am a Python beginner and wrote a function for a simple moving average strategy. I created a portfolio DataFrame inside the function and now I want to use this DataFrame outside of the function for plotting some graphs. My solution is: return portfolio - but this does not work. Can anybody help me?
This is my code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Import a data source - FSE-Data with Index 'Date'
all_close_prices = pd.read_csv('FSE_daily_close.csv')
all_close_prices = all_close_prices.set_index('Date')
# Fill NaN Values with the last available stock price - except for Zalando
all_close_prices = all_close_prices.fillna(method='ffill')
# Import ticker symbols
ticker_list = list(all_close_prices)
# Zalando 'FSE/ZO1_X' (position row 99) - doesn't begin in 2004
# Drop Zalando
all_close_prices.drop('FSE/ZO1_X', axis=1)
# Also from the ticker list
ticker_list.remove('FSE/ZO1_X')
# Create an empty signal dataframe with datetime index equivalent to the stocks
signals = pd.DataFrame(index=all_close_prices.index)
def ma_strategy(ticker, long_window, short_window):
# Calculate the moving avergaes
moving_avg_long = all_close_prices.rolling(window=long_window, min_periods=1).mean()
moving_avg_short = all_close_prices.rolling(window=short_window, min_periods=1).mean()
moving_avg_short = moving_avg_short
moving_avg_long = moving_avg_long
# Add the two MAs for the stocks in the ticker_list to the signals dataframe
for i in ticker_list:
signals['moving_avg_short_' + i] = moving_avg_short[i]
signals['moving_avg_long_' + i] = moving_avg_long[i]
# Set up the signals
for i in ticker_list:
signals['signal_' + i] = np.where(signals['moving_avg_short_' + i] > signals['moving_avg_long_' + i], 1, 0)
signals['positions_' + i] = signals['signal_' + i].diff(periods=1)
#Backtest
initial_capital = float(100000)
# Create a DataFrame `positions` with index of signals
positions = pd.DataFrame(index=all_close_prices)
# Create a new column in the positions DataFrame
# On the days that the signal is 1 (short moving average crosses the long moving average, you’ll buy a 100 shares.
# The days on which the signal is 0, the final result will be 0 as a result of the operation 100*signals['signal']
positions = 100 * signals[['signal_' + ticker]]
# Store the portfolio value owned with the stock
# DataFrame.multiply(other, axis='columns', fill_value=None) - Multiplication of dataframe and other, element-wise
# Store the difference in shares owned - same like position column in signals
pos_diff = positions.diff()
# Add `holdings` to portfolio
portfolio = pd.DataFrame(index=all_close_prices.index)
portfolio['holdings'] = (positions.multiply(all_close_prices[ticker], axis=0)).sum(axis=1)
# Add `cash` to portfolio
portfolio['cash'] = initial_capital - (pos_diff.multiply(all_close_prices[ticker], axis=0)).sum(
axis=1).cumsum()
# Add `total` to portfolio
portfolio['total'] = portfolio['cash'] + portfolio['holdings']
# Add `returns` to portfolio
portfolio['return'] = portfolio['total'].pct_change()
portfolio['return_cum'] = portfolio['total'].pct_change().cumsum()
return portfolio
ma_strategy('FSE/VOW3_X',20,5)
# Visualize the total value of the portfolio
portfolio_value = plt.figure(figsize=(12, 8))
ax1 = portfolio_value.add_subplot(1, 1, 1, ylabel='Portfolio value in $')
# Plot the equity curve in dollars
portfolio['total'].plot(ax=ax1, lw=2.)

You need to assign your function return value to a variable. The line which says
ma_strategy('FSE/VOW3_X',20,5)
probably needs to change to
portfolio = ma_strategy('FSE/VOW3_X',20,5)