mirror of
https://github.com/OpenBB-finance/OpenBB.git
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55fc1c56fd
* Fix backtesting not working on polygon data * Acknowledge lack of support for intraday data
270 lines
7.0 KiB
Python
270 lines
7.0 KiB
Python
"""Backtesting Model"""
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__docformat__ = "numpy"
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import logging
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import bt
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import pandas as pd
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import pandas_ta as ta
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import yfinance as yf
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.helper_funcs import is_intraday
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logger = logging.getLogger(__name__)
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@log_start_end(log=logger)
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def get_data(ticker: str, start_date: str) -> pd.DataFrame:
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"""Function to replace bt.get, Gets Adjusted close of ticker using yfinance
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Parameters
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----------
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ticker: str
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Ticker to get data for
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start_date: str
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Start date
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Returns
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-------
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prices: pd.DataFrame
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Dataframe of Adj Close with columns = [ticker]
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"""
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prices = yf.download(ticker, start=start_date, progress=False)
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prices = pd.DataFrame(prices["Adj Close"])
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prices.columns = [ticker]
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return prices
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@log_start_end(log=logger)
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def buy_and_hold(ticker: str, start_date: str, name: str) -> bt.Backtest:
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"""Generates a buy and hold backtest object for the given ticker
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Parameters
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----------
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ticker: str
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Stock to test
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start:str
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Backtest start date. Can be either string or datetime
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name: str
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Name of the backtest (for labeling purposes)
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Returns
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-------
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bt.Backtest
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Backtest object for buy and hold strategy
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"""
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prices = get_data(ticker, start_date)
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bt_strategy = bt.Strategy(
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name,
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[
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bt.algos.RunOnce(),
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bt.algos.SelectAll(),
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bt.algos.WeighEqually(),
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bt.algos.Rebalance(),
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],
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)
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return bt.Backtest(bt_strategy, prices)
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@log_start_end(log=logger)
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def ema_strategy(
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ticker: str,
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df_stock: pd.DataFrame,
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ema_length: int,
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spy_bt: bool = True,
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no_bench: bool = False,
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) -> bt.backtest.Result:
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"""Perform backtest for simple EMA strategy. Buys when price>EMA(l)
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Parameters
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----------
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ticker : str
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Stock ticker
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df_stock : pd.DataFrame
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Dataframe of prices
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ema_length : int
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Length of ema window
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spy_bt : bool
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Boolean to add spy comparison
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no_bench : bool
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Boolean to not show buy and hold comparison
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Returns
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-------
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bt.backtest.Result
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Backtest results
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"""
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# TODO: Help Wanted!
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# Implement support for backtesting on intraday data
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if is_intraday(df_stock):
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return None
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df_stock.index = pd.to_datetime(df_stock.index.date)
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ticker = ticker.lower()
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ema = pd.DataFrame()
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start_date = df_stock.index[0]
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prices = pd.DataFrame(df_stock["Adj Close"])
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prices.columns = [ticker]
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ema[ticker] = ta.ema(prices[ticker], ema_length)
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bt_strategy = bt.Strategy(
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"AboveEMA",
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[
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bt.algos.SelectWhere(prices >= ema),
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bt.algos.WeighEqually(),
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bt.algos.Rebalance(),
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],
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)
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bt_backtest = bt.Backtest(bt_strategy, prices)
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backtests = [bt_backtest]
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if spy_bt:
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spy_bt = buy_and_hold("spy", start_date, "SPY Hold")
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backtests.append(spy_bt)
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if not no_bench:
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stock_bt = buy_and_hold(ticker, start_date, ticker.upper() + " Hold")
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backtests.append(stock_bt)
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res = bt.run(*backtests)
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return res
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@log_start_end(log=logger)
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def ema_cross_strategy(
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ticker: str,
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df_stock: pd.DataFrame,
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short_length: int,
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long_length: int,
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spy_bt: bool = True,
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no_bench: bool = False,
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shortable: bool = True,
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) -> bt.backtest.Result:
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"""Perform backtest for simple EMA strategy. Buys when price>EMA(l)
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Parameters
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----------
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ticker : str
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Stock ticker
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df_stock : pd.DataFrame
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Dataframe of prices
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short_length : int
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Length of short ema window
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long_length : int
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Length of long ema window
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spy_bt : bool
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Boolean to add spy comparison
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no_bench : bool
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Boolean to not show buy and hold comparison
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shortable : bool
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Boolean to allow for selling of the stock at cross
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Returns
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-------
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Result
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Backtest results
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"""
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ticker = ticker.lower()
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start_date = df_stock.index[0]
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prices = pd.DataFrame(df_stock["Adj Close"])
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prices.columns = [ticker]
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short_ema = pd.DataFrame(ta.ema(prices[ticker], short_length))
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short_ema.columns = [ticker]
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long_ema = pd.DataFrame(ta.ema(prices[ticker], long_length))
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long_ema.columns = [ticker]
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# signals
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signals = long_ema.copy()
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signals[short_ema > long_ema] = 1.0
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signals[short_ema <= long_ema] = -1.0 * shortable
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signals[long_ema.isnull()] = 0.0
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combined_data = bt.merge(signals, prices, short_ema, long_ema)
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combined_data.columns = ["signal", "price", "ema_short", "ema_long"]
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bt_strategy = bt.Strategy(
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"EMA_Cross",
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[
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bt.algos.WeighTarget(signals),
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bt.algos.Rebalance(),
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],
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)
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bt_backtest = bt.Backtest(bt_strategy, prices)
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backtests = [bt_backtest]
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if spy_bt:
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spy_bt = buy_and_hold("spy", start_date, "SPY Hold")
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backtests.append(spy_bt)
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if not no_bench:
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stock_bt = buy_and_hold(ticker, start_date, ticker.upper() + " Hold")
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backtests.append(stock_bt)
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res = bt.run(*backtests)
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return res
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@log_start_end(log=logger)
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def rsi_strategy(
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ticker: str,
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df_stock: pd.DataFrame,
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periods: int,
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low_rsi: int,
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high_rsi: int,
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spy_bt: bool = True,
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no_bench: bool = False,
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shortable: bool = True,
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) -> bt.backtest.Result:
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"""Perform backtest for simple EMA strategy. Buys when price>EMA(l)
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Parameters
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----------
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ticker : str
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Stock ticker
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df_stock : pd.DataFrame
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Dataframe of prices
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periods : int
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Number of periods for RSI calculation
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low_rsi : int
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Low RSI value to buy
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hirh_rsi : int
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High RSI value to sell
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spy_bt : bool
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Boolean to add spy comparison
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no_bench : bool
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Boolean to not show buy and hold comparison
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shortable : bool
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Flag to disable the ability to short sell
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Returns
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-------
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Result
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Backtest results
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"""
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ticker = ticker.lower()
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start_date = df_stock.index[0]
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prices = pd.DataFrame(df_stock["Adj Close"])
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prices.columns = [ticker]
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rsi = pd.DataFrame(ta.rsi(prices[ticker], periods))
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rsi.columns = [ticker]
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signal = 0 * rsi.copy()
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signal[rsi > high_rsi] = -1 * shortable
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signal[rsi < low_rsi] = 1
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signal[rsi.isnull()] = 0
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merged_data = bt.merge(signal, prices)
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merged_data.columns = ["signal", "price"]
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bt_strategy = bt.Strategy(
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"RSI Reversion", [bt.algos.WeighTarget(signal), bt.algos.Rebalance()]
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)
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bt_backtest = bt.Backtest(bt_strategy, prices)
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bt_backtest = bt.Backtest(bt_strategy, prices)
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backtests = [bt_backtest]
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if spy_bt:
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spy_bt = buy_and_hold("spy", start_date, "SPY Hold")
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backtests.append(spy_bt)
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if not no_bench:
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stock_bt = buy_and_hold(ticker, start_date, ticker.upper() + " Hold")
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backtests.append(stock_bt)
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res = bt.run(*backtests)
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return res
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