mirror of
https://github.com/OpenBB-finance/OpenBB.git
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ee34a1f278
* Fixed isort * Fixed issues * FIxed linters --------- Co-authored-by: James Maslek <jmaslek11@gmail.com>
292 lines
7.9 KiB
Python
292 lines
7.9 KiB
Python
"""Backtesting Model"""
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__docformat__ = "numpy"
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import logging
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import warnings
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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.common.technical_analysis import ta_helpers
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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(symbol: str, start_date: str = "2019-01-01") -> pd.DataFrame:
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"""Function to replace bt.get, gets Adjusted close of symbol using yfinance.
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Parameters
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----------
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symbol: str
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Ticker to get data for
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start_date: str
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Start date in YYYY-MM-DD format
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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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data = yf.download(symbol, start=start_date, progress=False, ignore_tz=True)
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close_col = ta_helpers.check_columns(data, high=False, low=False)
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if close_col is None:
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return pd.DataFrame()
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df = pd.DataFrame(data[close_col])
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df.columns = [symbol]
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return df
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@log_start_end(log=logger)
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def buy_and_hold(symbol: 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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symbol: str
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Stock to test
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start_date: str
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Backtest start date, in YYYY-MM-DD format. 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(symbol, 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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symbol: str,
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data: pd.DataFrame,
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ema_length: int = 20,
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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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symbol: str
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Stock ticker
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data: 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(data):
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return None
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data.index = pd.to_datetime(data.index.date)
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symbol = symbol.lower()
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ema = pd.DataFrame()
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start_date = data.index[0]
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close_col = ta_helpers.check_columns(data, high=False, low=False)
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if close_col is None:
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return bt.backtest.Result()
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prices = pd.DataFrame(data[close_col])
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prices.columns = [symbol]
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ema[symbol] = ta.ema(prices[symbol], 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(symbol, start_date, symbol.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 emacross_strategy(
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symbol: str,
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data: pd.DataFrame,
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short_length: int = 20,
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long_length: int = 50,
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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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symbol : str
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Stock ticker
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data : 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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symbol = symbol.lower()
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start_date = data.index[0]
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close_col = ta_helpers.check_columns(data, low=False, high=False)
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if close_col is None:
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return bt.backtest.Result()
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prices = pd.DataFrame(data[close_col])
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prices.columns = [symbol]
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short_ema = pd.DataFrame(ta.ema(prices[symbol], short_length))
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short_ema.columns = [symbol]
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long_ema = pd.DataFrame(ta.ema(prices[symbol], long_length))
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long_ema.columns = [symbol]
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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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"EMACross",
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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(symbol, start_date, symbol.upper() + " Hold")
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backtests.append(stock_bt)
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore")
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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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symbol: str,
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data: pd.DataFrame,
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periods: int = 14,
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low_rsi: int = 30,
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high_rsi: int = 70,
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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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symbol : str
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Stock ticker
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data : 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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high_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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symbol = symbol.lower()
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start_date = data.index[0]
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close_col = ta_helpers.check_columns(data, high=False, low=False)
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if close_col is None:
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return pd.DataFrame()
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prices = pd.DataFrame(data[close_col])
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prices.columns = [symbol]
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rsi = pd.DataFrame(ta.rsi(prices[symbol], periods))
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rsi.columns = [symbol]
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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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warnings.simplefilter(action="ignore", category=FutureWarning)
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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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# Once the bt package replaces pd iteritems with items we can remove this
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with warnings.catch_warnings():
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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(symbol, start_date, symbol.upper() + " Hold")
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backtests.append(stock_bt)
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore")
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res = bt.run(*backtests)
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return res
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