@Z also just noticed that the very first file of my SP500 folder doesn't have data until 1999-11-18. Undoubtedly the issue has something to do with this. I will look again tomorrow, but if anybody has the solution in the meantime I would appreciate it!

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To plot each equity in a separate window, you need to loop through the strategies after running cerebro and call plot on each one individually:

strats = cerebro.run() for i, strat in enumerate(strats): cerebro.plot(stratname=datalist[i][1], figsize=(15,8))

This will plot each strategy in a separate figure, with the name of the equity as the title.

The key points:

cerebro.run() returns a list of strategy instances You can loop through this list and access each strategy Call cerebro.plot() and pass the stratname to use as the plot title Adjust the figsize as needed to control the size of each plot

So this will give you a separate plot for each equity in your basket, rather than packing them all into one figure or splitting by time period.

Let me know if that helps or if you have any other questions here: [https://www.qedge.co/](link url) or search "qedge"

Thanks for sharing this. It will be more helpful for me. You can also read more there.

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It looks like you're trying to use the YahooFinanceCSVData feed from the backtrader library to load cryptocurrency price data from a CSV file in Google Colab. However, it seems you might be facing difficulties or confusion based on the error message you've mentioned. Let's go through the code and address any potential issues.

The error message you posted:

<backtrader.feeds.yahoo.YahooFinanceCSVData at 0x7f6380b2b860>

This message doesn't indicate an error. It appears to be the representation of the YahooFinanceCSVData object in memory. It's not an error message, but rather a confirmation that the data object was created successfully.

However, if you're facing an issue when running the code, it's likely related to the CSV file or the way you're trying to run it in Google Colab.

Here are a few things to check and consider:

CSV File Format: Make sure that the CSV file you're trying to load (BTC-USD.csv) is in the correct format and contains valid cryptocurrency price data. The CSV file should have columns like 'Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close' with appropriate data.

File Path: Ensure that the BTC-USD.csv file is located in the current working directory of your Colab environment. You can verify this by using the !ls command to list the files in the current directory.

File Content: Open the CSV file and check if it's correctly formatted. Ensure that the column names and data are consistent and without any formatting issues.

Initialization: You're using the btfeeds.YahooFinanceCSVData class, but if you're loading data from a CSV file, you should use btfeeds.GenericCSVData instead.

Here's how you might modify your code to use GenericCSVData:

import backtrader as bt import backtrader.feeds as btfeeds # Define the parameters for the CSV data data_params = dict( dataname='BTC-USD.csv', # Make sure the file is in the current working directory datetime=0, # Column index of the datetime column (e.g., 'Date') open=1, high=2, low=3, close=4, volume=5, openinterest=-1, ) data = btfeeds.GenericCSVData(**data_params) cerebro = bt.Cerebro() cerebro.adddata(data) # Now you can proceed with strategy and analysis

Running in Colab: Make sure you have the necessary libraries (backtrader and any dependencies) installed in your Colab environment. If not, you can install them using !pip install backtrader.

Error Message: If you encounter any error messages, please provide the specific error message so that I can assist you more effectively.

By following these steps and considering these factors, you should be able to successfully load cryptocurrency money data from a CSV file in Google Colab using the backtrader library. If you encounter any specific error messages, feel free to share them for further assistance.

I found a solution....!

Being a novice, it will certainly be improved.

########################################################################### # # I would like to plot the previous day's High and Low in the hourly chart. # ########################################################################### import pandas as pd import numpy as np import yfinance as yf import backtrader as bt import backtrader.feeds as btfeeds import datetime as dt # import pytz df_1h = yf.download("EURUSD=X", period='3mo', interval='1h') # df_1h.head(3) # Remove the time zone offset df_1h.index = df_1h.index.tz_convert(None) ### Resemple Daily # Resample to daily data daily_data = df_1h.resample('1D').agg({ 'Open': 'first', 'High': 'max', 'Low': 'min', 'Close': 'last', }) # daily_data ## Calculate High Low Shift points on daily daily_data["High_Dy"] = daily_data["High"].shift(1) daily_data["Low_Dy"] = daily_data["Low"].shift(1) # Just keep the two shifted columns and delete NaN daily_data.dropna(inplace=True) daily_data = daily_data[["High_Dy", "Low_Dy"]] # Merging df_1h e hourly_data = df_1h.copy() plot_data = pd.merge_asof(hourly_data, daily_data, left_index=True, right_index=True) plot_data.dropna(inplace=True) # Insert 0 in the volume column, insert OpenInterest = 0, plot_data['Volume'] = 0 plot_data.insert(5, "OpenInterest", 0) plot_data.drop('Adj Close', axis=1, inplace=True) ######## Dynamic class creation ########## lines = ('High_Dy', 'Low_Dy') params = (('High_Dy', -1), ('Low_Dy', -1), ) datafields = btfeeds.PandasData.datafields + ( ['High_Dy', 'Low_Dy']) mydict = dict( lines=tuple(lines), params=params, datafields=bt.feeds.PandasData.datafields + list(lines), ) PandasDataOptix = type('PandasDataOptix', (btfeeds.PandasData,), mydict) class StrategyOptix(bt.Strategy): def __init__(self): # Plot High & Low Daily self.hi_dy = bt.ind.SMA(self.data.lines.High_Dy, period = 1) self.lo_dy = bt.ind.SMA(self.data.lines.Low_Dy, period = 1) def next(self): pass # Create a cerebro entity cerebro = bt.Cerebro() # Add a strategy cerebro.addstrategy(StrategyOptix) # Pass it to the backtrader datafeed and add it to the cerebro PandasDataOptix data = PandasDataOptix(dataname=plot_data) cerebro.adddata(data) # Run over everything cerebro.run() cerebro.plot(style='candle', iplot=False, volume = False, barupfill = False, bardownfill = False, barup='green', bardown='red')