David's Finance Package (dfinance) README

The goal of this package is to provide all the functionality necessary for creating, backtesting, and executing live algorithmic trading strategies.

Done:

• Data feed for historical price data
• Data feed for live price data
• Porfolio class to provide buy/sell helper functions and track transactions, cash account value, and current market positions
• Backtest class to provide the ability to backtest a trading strategy on historical data
• Sample historical data trading function (strategy) that operates on a Porfolio object
• Backtest class augmented to provide the ability to process live data and prefill historical data before live data
• Sample live (paper) data trading function (strategy) that operates with API calls to Alpaca instead of using a Porfolio object
• Train model for predicting price movement
• Incorporate model predictions as part of a live/paper trading strategy
  • Historical data backtest of this strategy
  • Live/paper trading using this strategy

To do:

• Write documentation on RobinHood functionality
• Write documentation on AlpacaPortfolio class
• Build additional data feeds for alternative data
• Write documentation on htpeter's utils.Ledger class

Import the library

import dfinance as dfin

Import a strategy

from dfinance.trading_strategies import Strategy_SMA_Crossover

---

Backtest Class

Initialize the backtest object

my_back = dfin.Backtest()

Run a strategy on historical data

my_back.process_historical_data(historical_df,                      # dataframe with price data
                                my_port,                            # portfolio object
                                Strategy_SMA_Crossover.strategy,    # strategy function name
                                sma_short=10,                       # keyword arg for this strategy
                                sma_long=15,                        # keyword arg for this strategy
                                share_num=50,                       # keyword arg for this strategy
                                ticker='AAPL')                      # keyword arg for this strategy

Run a strategy on live data

Under construction.

---

Portfolio Class

Initialize the portfolio object

my_port = dfin.Portfolio()

Set the cash value

my_port.cashvalue = 133_700.00

Get ledger of transactions or summary of transactions

my_port.ledger()

my_port.summary_of_transactions()

Get balance of cash in the account and value in the market

my_port.cashvalue

my_port.value_in_market()

Save transactions or load transactions

my_port.save_transactions('myport.csv')

my_port.load_transactions('myport.csv')

Buy or sell stock

Arguments: ticker, share count, share price, transaction datetime (as a string)

my_port.buy_stock('GME', 2, 10, '2021-01-01 00:00:00')

my_port.sell_stock('GME', 1, 20, '2022-01-01 00:12:00')