README.md

DCAshund 🐕

Long on strategy, short on complexity, your new loyal and compact DCA companion.

Disclaimer

This tool is provided for educational and informational purposes only and should not be construed as investment advice. Investing involves risk that could result in financial losses. Past performance is not indicative of future results. Always do your own research and consider your financial situation carefully before making investment decisions.

Installation

I highly recommand you to install Mamba (https://github.com/conda-forge/miniforge#mambaforge) if you want a clean python environnment. To install everything properly with mamba, you just have to do this:

$ git clone https://github.com/jeeberhardt/dcashund
$ cd dcashund
$ mamba env create -f environment.yaml -n dcashund
$ mamba activate dcashund
$ pip install .

Quick tutorial

Here's a step-by-step guide on how to use DCAshund for simulating DCA (Dollar Cost Averaging) strategies:

1. Import necessary modules: We'll need yfinance for retrieving ETF data, pandas and several modules from DCAshund:

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#

import yfinance as yf
import pandas as pd

from dcashund import DCAshund, merge_stock_data, plot_simulation

2. Retrieve and save ETF data: We'll use yfinance to retrieve historical data for certain ETFs and save the data as CSV files. Replace the ISINS with those of the ETFs you're interested in:

isins = [('FR0010315770', 'Lyxor MSCI World UCITS ETF - Dist'),
         ('LU0533033667', 'Lyxor MSCI World Information Technology ETF - Dist')]

for fund in isins:
    ticker = yf.Ticker(fund[0])
    history = ticker.history(period="max")

    # Remove hour from date
    history['Date'] = pd.to_datetime(history.index).date
    history.set_index('Date', inplace=True)

    if not history.empty:
        history.to_csv(f'{fund[0]}.csv')

3. Merge stock data: If you have data from multiple ETFs, you can use merge_stock_data to combine them into one dataframe:

merged_data = merge_stock_data(['FR0010315770.csv', 'LU0533033667.csv'])

4. Now we're ready to simulate some DCA strategies. We'll initialize a DCAshund object, simulate a strategy, and plot the results:

• The weights parameter represents the proportion of each investment in the portfolio. The order of the weights corresponds to the order of the asset data files provided in merge_stock_data.
• The dca parameter represents the amount of money invested in each period for the DCA strategy.
• The entry_fee parameter represents the transaction fees associated with buying assets, expressed as a proportion of the transaction amount.

# We are going to use the Close prices
dh = DCAshund('Close')

# Here we invest 500 euros (85 % in FR0010315770 and 15 % in LU0533033667) 
# on the fourth day of each month, considering an entry fee of 1 %.
results = dh.simulate(merged_data, '2019-03-04', '2023-07-14', weights=[0.85, 0.15], dca=500, entry_fee=1./100)

# Plot the simulation result
plot_simulation(results)

That's it! You've now learned how to use DCAshund to simulate DCA strategies. Remember, investing involves risk. Always do your own research before making investment decisions.