import yfinance as yf
import pandas as pd
import talib
code = '2800'
para_dict = {
'sample_period_list': [200],
'fastperiod_list': [12, 16],
'slowperiod_list': [26, 30],
'signalperiod_list': [8, 12],
'higher_percentile_list': [0.8],
'profit_target': [0.04],
'stop_loss': [-0.04]
}
start_date = '2020-01-01'
end_date = '2022-10-10'
df_dict = {}
df = yf.Ticker(code + '.HK').history(start=start_date, end=end_date)
df = df[df['Volume'] > 0]
df = df[['Open', 'High', 'Low', 'Close']]
# df['pnl_percentage'] = df['Open'].pct_change()
df = df.reset_index()
for fastperiod in para_dict['fastperiod_list']:
for slowperiod in para_dict['slowperiod_list']:
for signalperiod in para_dict['signalperiod_list']:
macd_key = str(fastperiod) + '_' + str(slowperiod) + '_' + str(signalperiod)
df['macd'], df['macdsignal'], df['macdhist'] = talib.MACD(df['Close'], fastperiod=fastperiod, slowperiod=slowperiod, signalperiod=signalperiod)
df_dict[macd_key] = df
print(df_dict)
I cant get the right dataframe for different MACD periods, instead I generate the same dataframe using different MACD periods by below codes? WHY
I cant get the right dataframe for different MACD periods, instead I generate the same dataframe using different MACD periods by below codes? WHY
The reason is because you're pointing to the same dataframe , if you change one they all change so in your example they will be all equal to the last df.
you can read more in it in those questions :
Modifying one dataframe appears to change another
Why can pandas DataFrames change each other?
As a solution to your case , you need to use a copy of the dataframe not the actual dataframe :
df_dict[macd_key] = df.copy()
#instead of df_dict[macd_key] = df
it will solve your issue
I have a table like this (with more columns):
date,Sector,Value1,Value2
14/03/22,Medical,86,64
14/03/22,Medical,464,99
14/03/22,Industry,22,35
14/03/22,Services,555,843
15/03/22,Services,111,533
15/03/22,Industry,222,169
15/03/22,Medical,672,937
15/03/22,Medical,5534,825
I have created some features like this:
sectorGroup = df.groupby(["date","Sector"])["Value1","Value2"].mean().reset_index()
df = pd.merge(df,sectorGroup,on=["date","Sector"],how="left",suffixes=["","_bySector"])
dateGroupGroup = df.groupby(["date"])["Value1","Value2"].mean().reset_index()
df = pd.merge(df,dateGroupGroup,on=["date"],how="left",suffixes=["","_byDate"])
Now my new df looks like this:
date,Sector,Value1,Value2,Value1_bySector,Value2_bySector,Value1_byDate,Value2_byDate
14/03/22,Medical,86,64,275.0,81.5,281.75,260.25
14/03/22,Medical,464,99,275.0,81.5,281.75,260.25
14/03/22,Industry,22,35,22.0,35.0,281.75,260.25
14/03/22,Services,555,843,555.0,843.0,281.75,260.25
15/03/22,Services,111,533,111.0,533.0,1634.75,616.0
15/03/22,Industry,222,169,222.0,169.0,1634.75,616.0
15/03/22,Medical,672,937,3103.0,881.0,1634.75,616.0
15/03/22,Medical,5534,825,3103.0,881.0,1634.75,616.0
Now, I want to create lag features for Value1_bySector,Value2_bySector,Value1_byDate,Value2_byDate
For example, a new column named Value1_by_Date_lag1 and Value1_bySector_lag1.
And this new column will look like this:
date,Sector,Value1_by_Date_lag1,Value1_bySector_lag1
15/03/22,Services,281.75,555.0
15/03/22,Industry,281.75,22.0
15/03/22,Medical,281.75,275.0
15/03/22,Medical,281.75,275.0
Basically in Value1_by_Date_lag1, the date "15/03" will contain the value "281.75" which is for the date "14/03" (lag of 1 shift).
Basically in Value1_bySector_lag1, the date "15/03" and Sector "Medical" will contain the value "275.0", which is the value for "14/03" and "Medical" rows.
I hope, the question is clear and gave you all the details.
Create a lagged date variable by shifting the date column, and then merge again with dateGroupGroup and sectorGroup using the lagged date instead of the actual date.
df = pd.read_csv(io.StringIO("""date,Sector,Value1,Value2
14/03/22,Medical,86,64
14/03/22,Medical,464,99
14/03/22,Industry,22,35
14/03/22,Services,555,843
15/03/22,Services,111,533
15/03/22,Industry,222,169
15/03/22,Medical,672,937
15/03/22,Medical,5534,825"""))
# Add a lagged date variable
lagged = df.groupby("date")["date"].first().shift()
df = df.join(lagged, on="date", rsuffix="_lag")
# Create date and sector groups and merge them into df, as you already do
sectorGroup = df.groupby(["date","Sector"])[["Value1","Value2"]].mean().reset_index()
df = pd.merge(df,sectorGroup,on=["date","Sector"],how="left",suffixes=["","_bySector"])
dateGroupGroup = df.groupby("date")[["Value1","Value2"]].mean().reset_index()
df = pd.merge(df, dateGroupGroup, on="date",how="left", suffixes=["","_byDate"])
# Merge again, this time matching the lagged date in df to the actual date in sectorGroup and dateGroupGroup
df = pd.merge(df, sectorGroup, left_on=["date_lag", "Sector"], right_on=["date", "Sector"], how="left", suffixes=["", "_by_sector_lag"])
df = pd.merge(df, dateGroupGroup, left_on="date_lag", right_on="date", how="left", suffixes=["", "_by_date_lag"])
# Drop the extra unnecessary columns that have been created in the merge
df = df.drop(columns=['date_by_date_lag', 'date_by_sector_lag'])
This assumes the data is sorted by date - if not you will have to sort before generating the lagged date. It will work whether or not all the dates are consecutive.
I found 1 inefficient solution (slow and memory intensive).
Lag of "date" group
cols = ["Value1_byDate","Value2_byDate"]
temp = df[["date"]+cols]
temp = temp.drop_duplicates()
for i in range(10):
temp.date = temp.date.shift(-1-i)
df = pd.merge(df,temp,on="date",how="left",suffixes=["","_lag"+str(i+1)])
Lag of "date" and "Sector" group
cols = ["Value1_bySector","Value2_bySector"]
temp = df[["date","Sector"]+cols]
temp = temp.drop_duplicates()
for i in range(10):
temp[["Value1_bySector","Value2_bySector"]] = temp.groupby("Sector")["Value1_bySector","Value2_bySector"].shift(1+1)
df = pd.merge(df,temp,on=["date","Sector"],how="left",suffixes=["","_lag"+str(i+1)])
Is there a more simple solution?
On Pandas 1.3.4 and Python 3.9.
So I'm having issues filtering for a partial piece of the string. The "Date" column is listed in the format of MM/DD/YYYY HH:MM:SS A/PM where the most recent one is on top. If the date is single digit (example: November 3rd), it does not have the 0 such that it is 11/3 instead of 11/03. Basically I'm looking to go look at column named "Date" and have python read parts of the string to filter for only today.
This is what the original csv looks like. This is what I want to do to the file. Basically looking for a specific date but not any time of that date and implement the =RIGHT() formula. However this is what I end up with with the following code.
from datetime import date
import pandas as pd
df = pd.read_csv(r'file.csv', dtype=str)
today = date.today()
d1 = today.strftime("%m/%#d/%Y") # to find out what today is
df = pd.DataFrame(df, columns=['New Phone', 'Phone number', 'Date'])
df['New Phone'] = df['Phone number'].str[-10:]
df_today = df['Date'].str.contains(f'{d1}',case=False, na=False)
df_today.to_csv(r'file.csv', index=False)
This line is wrong:
df_today = df['Date'].str.contains(f'{d1}',case=False, na=False)
All you're doing there is creating a mask; essentially what that is is just a Pandas series, containg True or False in each row, according to the condition you created the mask in. The spreadsheet get's only FALSE as you showed because non of the items in the Date contain the string that the variable d1 holds...
Instead, try this:
from datetime import date
import pandas as pd
# Load the CSV file, and change around the columns
df = pd.DataFrame(pd.read_csv(r'file.csv', dtype=str), columns=['New Phone', 'Phone number', 'Date'])
# Take the last ten chars of each phone number
df['New Phone'] = df['Phone number'].str[-10:]
# Convert each date string to a pd.Timestamp, removing the time
df['Date'] = pd.to_datetime(df['Date'].str.split(r'\s+', n=1).str[0])
# Get the phone numbers that are from today
df_today = df[df['Date'] == date.today().strftime('%m/%d/%Y')]
# Write the result to the CSV file
df_today.to_csv(r'file.csv', index=False)