def env_spy(seed=0):
def indicator1(df):
close = df[DF_ADJ_CLOSE]
windows = [100, 400]
outs = {}
for window in windows:
ts = TrailingStats(close, window)
out = ts.exp_growth
out[np.isnan(out)] = 0
outs[f'growth({window})'] = out
out = ts.exp_reg_diff
out[np.isnan(out)] = 0
outs[f'diff({window})'] = out
return outs
stock_data = YahooData(symbols=['SPY'])
indicators = Indicators(stock_data)
indicators.create(indicator1)
env = EnvBase(
stock_data=stock_data,
indicators=indicators,
price_name=DF_ADJ_CLOSE,
seed=seed,
commission=0,
)
env.reset()
return env
def env_noise(seed=0):
def indicator1(df):
close = df['Close']
windows = [20, 50, 100]
outs = {}
for window in windows:
ts = TrailingStats(close, window)
out = ts.exp_growth
out[np.isnan(out)] = 0
outs[f'growth({window})'] = out
out = ts.exp_reg_diff
out[np.isnan(out)] = 0
outs[f'diff({window})'] = out
return outs
fdata = FakeData(0, include=['Noise(1.0)'])
indicators = Indicators(fdata)
indicators.create(indicator1, )
env = EnvBase(stock_data=fdata,
indicators=indicators,
price_name='Close',
seed=seed)
env.reset()
return env
def test_indicator():
y = YahooData(['DIS', 'GOOG'])
indicators = Indicators(y)
indicators.create(trailing_avg, 5, name='trailing')
df1 = indicators.dataframes['DIS']
df2 = indicators.dataframes['GOOG']
assert 'trailing' in df1.columns
assert 'trailing' in df2.columns
def env_sin20_growth():
"""Test environment for simple, smooth sin wave with trailing growth rate
as indicators."""
def indicator1(df):
close = df['Close']
windows = [5, 10, 20, 50]
outs = {}
for window in windows:
ts = TrailingStats(close, window)
out = ts.exp_growth
# Replace NAN with 0
out[np.isnan(out)] = 0
outs[f'{window}'] = out
return outs
fdata = FakeData(0, include=['Sin(20)'])
indicators = Indicators(fdata)
indicators.create(indicator1, name='growth')
env = EnvBase(stock_data=fdata, indicators=indicators, price_name='Close')
env.reset()
return env
if np.isnan(out):
out = -1
return out
def stop_loss(df: pd.DataFrame):
series = df[DF_ADJ_CLOSE]
ts = TrailingStats(series, window_size=200)
loss = ts.max_loss
gain = ts.max_gain
delta = gain - loss
delta = append_nan(delta, ts.window_size)
return delta
indicator_stoploss = Indicators(yahoo)
indicator_stoploss.create(stop_loss)
table_stoploss = indicator_stoploss.tables['SPY']
STOCKS = symbols
# STOCKS.append('SPY')
# STOCKS.append('VOO')
# STOCKS.append('GOOG')
# STOCKS.append('TSLA')
STOCKS = np.array(STOCKS)
# rng = np.random.default_rng(1)
# rng.shuffle(STOCKS)
# STOCKS = STOCKS[0:200]
# %% Strategies
STOCKS.append('SPY')
STOCKS.append('VOO')
# STOCKS.append('GOOG')
# STOCKS.append('TSLA')
STOCKS = np.array(STOCKS)
def post1(df: pd.DataFrame):
series = df[DF_ADJ_CLOSE]
ts = TrailingStats(series, 252 * 5)
stat1 = ts.exp_growth
return stat1
yahoo.symbol_names = STOCKS
indicator = Indicators(yahoo)
indicator.create(post1)
df = indicator.extract_column('post1()')
table = TableData(df)
index_spy = np.where(df.columns == 'SPY')[0][0]
# %%
class Strat1(Strategy):
def init(self):
# Build long and short rate metric
self.growths = []
# Arbitrarily set initial stock. Doesn't matter.
self.current_stocks = []
# 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)
metric = ts.max_gain - ts.max_loss
return metric
yahoo.symbols = STOCKS
indicator = Indicators(yahoo)
indicator.create(post1)
df = indicator.get_column_from_all('post1()')
# %%
table = TableData(df)
# %%
class Strat1(Strategy):
def init(self):
# Build long and short rate metric
self.growths = []
return d
def post2(df:pd.DataFrame):
series = df[DF_ADJ_CLOSE]
ts = TrailingStats(series, 8)
slope, interc, rvalue = ts.linear_regression
r2 = rvalue**2
metric = slope * r2
std = cumulative_std(metric)
return metric / std
yahoo.symbols = STOCKS
indicator = Indicators(yahoo)
indicator.create(post2, name='p')
# df = indicator.get_column_from_all('post1()')
df = indicator.get_symbol_all('SPY')
table = TableData(df)
# %%
class Strat1(Strategy):
def init(self):
# Build long and short rate metric
self.growths = []
from backtester.stockdata import YahooData, Indicators
from backtester.indicators import TrailingStats
from backtester.definitions import DF_ADJ_CLOSE
import matplotlib.pyplot as plt
def my_return(df):
s = df[DF_ADJ_CLOSE]
size = 252
ts = TrailingStats(s, window_size=size)
return ts.return_ratio * 252 / size
y = YahooData()
date1 = np.datetime64('2021-05-01')
date0 = date1 - np.timedelta64(6 * 365, 'D')
indicator = Indicators(y)
indicator.create(my_return, name='return(Yr)')
df = indicator.extract_column('return(Yr)')
ii = (df.index.values >= date0) & (df.index.values <= date1)
df2 = df.iloc[ii]
descr = df2.describe().sort_values(by='50%', axis=1, ascending=False)
# %%
plt.plot(y.dataframes['ALGN'][DF_ADJ_CLOSE])
ax = plt.gca()
ax.set_yscale('log')