def fn_growth(df: pd.DataFrame, window_size):
v = df[DF_ADJ_CLOSE]
t = TrailingStats(v, window_size)
return t.exp_growth
def fn_std_change(df: pd.DataFrame, window_size):
v = df[DF_ADJ_CLOSE]
t = TrailingStats(v, window_size)
return t.exp_std_dev
yahoo = YahooData()
stocks = yahoo.get_symbol_names()
rs = np.random.RandomState(10)
rs.shuffle(stocks)
stocks = stocks[0:20]
stocks = stocks + ['SPY']
class Indicators:
def __init__(self,
stocks: list[str],
func: Callable,
fargs=(),
fkwargs=None):
self.stocks = stocks
self.func = func
self.fargs = fargs
from backtester.stockdata import YahooData
from backtester.exceptions import NotEnoughDataError
import datetime
def func1(window_size, df: pd.DataFrame):
v = df[DF_ADJ_CLOSE]
t = TrailingStats(v, window_size)
volatility = t.exp_std_dev
rate = t.exp_growth
pdb.set_trace()
return rate, volatility
yahoo_data = YahooData()
all_names = yahoo_data.get_symbol_names()
np.random.seed(0)
np.random.shuffle(all_names)
stocks1 = all_names[0 : 16]
stocks = []
for stock in stocks1:
if len(yahoo_data[stock]) > 0:
stocks.append(stock)
yahoo_data = YahooData(symbols=stocks)
class Strat1(Strategy):
def init(self):
# Build long and short rate metrics
self.ind1 = self.indicator(func1, 301, name='rate')
from typing import Callable
from backtester import Strategy, Backtest
from backtester.indicators import TrailingStats
from backtester.definitions import DF_ADJ_CLOSE
from backtester.stockdata import YahooData, TableData, Indicators
from backtester.exceptions import NotEnoughDataError
from functools import cached_property
import datetime
import matplotlib.pyplot as plt
from dotenv import load_dotenv
load_dotenv()
yahoo = YahooData()
symbols = yahoo.get_symbol_names()
rs = np.random.default_rng(0)
rs.shuffle(symbols)
# STOCKS = symbols[0:100]
# STOCKS.append('SPY')
# STOCKS.append('VOO')
# STOCKS.append('GOOG')
# STOCKS.append('TSLA')
STOCKS = ['SPY']
STOCKS = np.array(STOCKS)
def post1(df: pd.DataFrame):
series = df[DF_ADJ_CLOSE]
ts = TrailingStats(series, 100)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import backtester
from backtester.indicators import TrailingStats
from backtester.analysis import BuySell, avg_future_growth
from backtester.stockdata import YahooData
from backtester.definitions import DF_ADJ_CLOSE
from backtester import utils
from backtester.smooth import SmoothOptimize, TrailingSavGol
y = YahooData()
names = y.get_symbol_names()
np.random.seed(7)
np.random.shuffle(names)
symbol = 'MSFT'
date1 = np.datetime64('2005-01-01')
date2 = np.datetime64('2018-08-01')
df = y.get_symbol_before(symbol, date2)
ii = df.index.values >= date1
df = df.loc[ii]
close = df[DF_ADJ_CLOSE]
ts = TrailingStats(close, window_size=30)
growth = ts.exp_growth