def run(self):
self._ts = pf.fetch_timeseries(self._symbol)
self._ts = pf.select_tradeperiod(self._ts,
self._start,
self._end,
use_adj=False)
# Add technical indicator: 200 day sma
sma200 = SMA(self._ts, timeperiod=200)
self._ts['sma200'] = sma200
# Add technical indicator: X day high, and X day low
period_high = pd.Series(self._ts.close).rolling(self._period).max()
period_low = pd.Series(self._ts.close).rolling(self._period).min()
self._ts['period_high'] = period_high
self._ts['period_low'] = period_low
self._tlog = pf.TradeLog()
self._dbal = pf.DailyBal()
# add S&P500 200 sma
sp500 = pf.fetch_timeseries('^GSPC')
sp500 = pf.select_tradeperiod(sp500, self._start, self._end, False)
self._ts['sp500_close'] = sp500['close']
sp500_sma = SMA(sp500, timeperiod=200)
self._ts['sp500_sma'] = sp500_sma
self._algo()
def run(self):
self._ts = pf.fetch_timeseries(self._symbol)
self._ts = pf.select_tradeperiod(self._ts, self._start,
self._end, use_adj=True)
# Add technical indicator: 200 day sma
sma200 = SMA(self._ts, timeperiod=200)
self._ts['sma200'] = sma200
# Add technical indicator: X day sma
sma = SMA(self._ts, timeperiod=self._sma)
self._ts['sma'] = sma
# Add technical indicator: X day high, and X day low
period_high = pd.Series(self._ts.close).rolling(self._period).max()
period_low = pd.Series(self._ts.close).rolling(self._period).min()
self._ts['period_high'] = period_high
self._ts['period_low'] = period_low
self._ts, self._start = pf.finalize_timeseries(self._ts, self._start)
self._tlog = pf.TradeLog()
self._dbal = pf.DailyBal()
self._algo()
def run(self):
# Fetch and select timeseries.
self.ts = pf.fetch_timeseries(self.symbol,
use_cache=self.options['use_cache'])
self.ts = pf.select_tradeperiod(self.ts,
self.start,
self.end,
use_adj=self.options['use_adj'])
# Add technical indicator: 200 day sma regime filter.
self.ts['regime'] = pf.CROSSOVER(self.ts,
timeperiod_fast=1,
timeperiod_slow=200)
# Add technical indicators: X day high, and X day low.
self.ts['period_high'] = pd.Series(self.ts.close).rolling(
self.options['period']).max()
self.ts['period_low'] = pd.Series(self.ts.close).rolling(
self.options['period']).min()
# Finalize timeseries.
self.ts, self.start = pf.finalize_timeseries(self.ts, self.start)
# Create tlog and dbal objects.
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
# Run algo, get logs, and get stats.
self._algo()
self._get_logs()
self._get_stats()
def run(self):
self._ts = pf.fetch_timeseries(self._symbol)
self._ts = pf.select_tradeperiod(self._ts, self._start, self._end)
self._tlog = pf.TradeLog()
self._dbal = pf.DailyBal()
self._algo()
def run(self):
self.ts = pf.fetch_timeseries(self.symbol)
self.ts = pf.select_tradeperiod(self.ts, self.start, self.end, use_adj=True)
# Add technical indicator: 200 sma regime filter
self.ts['regime'] = \
pf.CROSSOVER(self.ts, timeperiod_fast=1, timeperiod_slow=200)
# Add technical indicator: instrument risk, i.e. annual std
self.ts['vola'] = \
pf.VOLATILITY(self.ts, lookback=20, time_frame='yearly')
# Add technical indicator: X day sma
sma = SMA(self.ts, timeperiod=self.sma)
self.ts['sma'] = sma
# Add technical indicator: X day high, and X day low
period_high = pd.Series(self.ts.close).rolling(self.period).max()
period_low = pd.Series(self.ts.close).rolling(self.period).min()
self.ts['period_high'] = period_high
self.ts['period_low'] = period_low
self.ts, self.start = pf.finalize_timeseries(self.ts, self.start)
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
self._algo()
def get_logs(self):
"""
Return raw tradelog, tradelog, and daily balance log.
Parameters
----------
None
Returns
-------
rlog : pd.DataFrame
The raw trade log.
tlog : pd.DataFrame
The trade log.
dbal : pd.DataFrame
The daily balance log.
"""
tlogs = []; rlogs = []
for tlog in pf.TradeLog.instance.values():
rlogs.append(tlog.get_log_raw())
tlogs.append(tlog.get_log(merge_trades=False))
rlog = pd.concat([r for r in rlogs]).sort_values(['seq_num'],
ignore_index=True)
tlog = pd.concat([t for t in tlogs]).sort_values(['entry_date', 'exit_date'],
ignore_index=True)
tlog['cumul_total'] = tlog['pl_cash'].cumsum()
dbal = pf.DailyBal()
dbal._l = self._l
dbal = dbal.get_log(tlog)
return rlog, tlog, dbal
def run(self):
self.ts = pf.fetch_timeseries(self.symbol,
use_cache=self.options['use_cache'])
self.ts = pf.select_tradeperiod(self.ts, self.start, self.end,
self.options['use_adj'])
# Add calendar columns
self.ts = pf.calendar(self.ts)
# Add momentum indicator for 3...18 months
lookbacks = range(3, 18 + 1)
for lookback in lookbacks:
self.ts['mom' + str(lookback)] = pf.MOMENTUM(self.ts,
lookback=lookback,
time_frame='monthly',
price='close',
prevday=False)
self.ts, self.start = pf.finalize_timeseries(self.ts, self.start)
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
self._algo()
self._get_logs()
self._get_stats()
def test_daily_balance_state(self):
''' For t to be instantiated the trade state must exist.
Throw an error if the trade state is not valid.
'''
bal = pf.DailyBal()
self.state = None
with self.assertRaises(pf.trade.TradeStateError):
self.call_append(bal)
def run(self):
self.ts = pf.fetch_timeseries(self.symbol)
self.ts = pf.select_tradeperiod(self.ts,
self.start,
self.end,
use_adj=self.use_adj)
self.ts, _ = pf.finalize_timeseries(self.ts, self.start)
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
self._algo()
def run(self):
self._ts = pf.fetch_timeseries(self._symbol)
self._ts = pf.select_tradeperiod(self._ts, self._start,
self._end, use_adj=False)
# Add technical indicator: day sma
sma = SMA(self._ts, timeperiod=self._sma_period)
self._ts['sma'] = sma
self._tlog = pf.TradeLog()
self._dbal = pf.DailyBal()
self._algo()
def run(self):
self._ts = pf.fetch_timeseries(self._symbol)
self._ts = pf.select_tradeperiod(self._ts,
self._start,
self._end,
use_adj=self._use_adj,
pad=False)
self._ts, _ = pf.finalize_timeseries(self._ts, self._start)
self._tlog = pf.TradeLog()
self._dbal = pf.DailyBal()
self._algo()
def run(self):
self._ts = pf.fetch_timeseries(self._symbol)
self._ts = pf.select_tradeperiod(self._ts,
self._start,
self._end,
use_adj=False)
# Add technical indicator: X day high
period_high = pd.rolling_max(self._ts.high, self._period)
self._ts['period_high'] = period_high
self._tlog = pf.TradeLog()
self._dbal = pf.DailyBal()
self._algo()
def run(self):
self.ts = pf.fetch_timeseries(self.symbol)
self.ts = pf.select_tradeperiod(self.ts, self.start,
self.end, self.use_adj)
# Add technical indicator: day sma regime filter
self.ts['regime'] = \
pf.CROSSOVER(self.ts, timeperiod_fast=1, timeperiod_slow=self.sma_period,
band=self.percent_band)
self.ts, self.start = pf.finalize_timeseries(self.ts, self.start)
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
self._algo()
def run(self):
self.ts = pf.fetch_timeseries(self.symbol)
self.ts = pf.select_tradeperiod(self.ts, self.start, self.end)
# add regime filter
self.ts['regime'] = \
pf.CROSSOVER(self.ts,
timeperiod_fast=self.timeperiod_fast,
timeperiod_slow=self.timeperiod_slow,
band=self.percent_band)
self.ts, self.start = pf.finalize_timeseries(self.ts, self.start)
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
self._algo()
def test_daily_balance_append(self):
''' Check that the daily log grows as expected. '''
bal = pf.DailyBal()
df = bal.get_log()
self.assertTrue(isinstance(df, pd.DataFrame))
self.assertTrue(len(df.index) == 0)
self.state = pf.trade.TradeState.OPEN
self.call_append(bal)
df = bal.get_log()
self.assertTrue(len(df.index) == 1)
self.state = pf.trade.TradeState.HOLD
self.call_append(bal)
df = bal.get_log()
self.assertTrue(len(df.index) == 2)
self.state = pf.trade.TradeState.CLOSE
self.call_append(bal)
df = bal.get_log()
self.assertTrue(len(df.index) == 3)
price_columns = ["high", "low", "close"]
other_columns = ["shares", "cash"]
# we are keeping the high, low and close the same for this test
# cash and shares are also kept equal
portfolio_value = self.high * self.shares + self.cash
# all prices are $x
prices = list(set(df[price_columns].values.flatten().tolist()))
self.assertTrue(len(prices) == 1)
self.assertTrue(prices[0] == portfolio_value)
# all other values (except state) are y
others = list(set(df[other_columns].values.flatten().tolist()))
self.assertTrue(len(others) == 1)
self.assertTrue(others[0] == self.shares)
# check the order of our states
states = df["state"].values.tolist()
self.assertTrue(states[0] == pf.trade.TradeState.OPEN)
self.assertTrue(states[1] == pf.trade.TradeState.HOLD)
self.assertTrue(states[2] == pf.trade.TradeState.CLOSE)
def run(self):
self._ts = pf.fetch_timeseries(self._symbol)
self._ts = pf.select_tradeperiod(self._ts, self._start,
self._end, self._use_adj)
# Add technical indicator: day sma
sma = SMA(self._ts, timeperiod=self._sma_period)
self._ts['sma'] = sma
self._tlog = pf.TradeLog()
self._dbal = pf.DailyBal()
# add S&P500 200 sma regime filter
ts = pf.fetch_timeseries('^GSPC')
ts = pf.select_tradeperiod(ts, self._start, self._end, False)
self._ts['regime'] = \
pf.CROSSOVER(ts, timeperiod_fast=1, timeperiod_slow=200)
self._algo()
def run(self):
self.ts = pf.fetch_timeseries(self.symbol)
self.ts = pf.select_tradeperiod(self.ts, self.start, self.end)
# add regime filter
self.ts['regime'] = \
pf.CROSSOVER(self.ts,
timeperiod_fast=self.timeperiod_fast,
timeperiod_slow=self.timeperiod_slow,
band=self.percent_band)
# Add technical indicator: volatility
self.ts['vola'] = pf.VOLATILITY(self.ts)
self.ts, self.start = pf.finalize_timeseries(self.ts, self.start)
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
self._algo()
def run(self):
self._ts = pf.fetch_timeseries(self._symbol)
self._ts = pf.select_tradeperiod(self._ts, self._start,
self._end, self._use_adj)
# Add technical indicator: day sma
sma = SMA(self._ts, timeperiod=self._sma_period)
self._ts['sma'] = sma
self._tlog = pf.TradeLog()
self._dbal = pf.DailyBal()
# add S&P500 200 sma
sp500 = pf.fetch_timeseries('^GSPC')
sp500 = pf.select_tradeperiod(sp500, self._start,
self._end, False)
self._ts['sp500_close'] = sp500['close']
sp500_sma = SMA(sp500, timeperiod=200)
self._ts['sp500_sma'] = sp500_sma
self._algo()
def run(self):
# Fetch and selct timeseries
self.ts = pf.fetch_timeseries(self.symbol,
use_cache=self.options['use_cache'])
self.ts = pf.select_tradeperiod(self.ts, self.start, self.end,
self.options['use_adj'])
# Add technical indicator: day sma regime filter.
self.ts['regime'] = \
pf.CROSSOVER(self.ts, timeperiod_fast=50, timeperiod_slow=200)
# Finalize timeseries
self.ts, self.start = pf.finalize_timeseries(self.ts, self.start)
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
self._algo()
self._get_logs()
self._get_stats()
def run(self):
self._ts = pf.fetch_timeseries(self._symbol)
self._ts = pf.select_tradeperiod(self._ts,
self._start,
self._end,
use_adj=False)
# Add technical indicator: 200 day sma
sma200 = SMA(self._ts, timeperiod=200)
self._ts['sma200'] = sma200
# Add technical indicator: X day high, and X day low
period_high = pd.rolling_max(self._ts.high, self._period)
period_low = pd.rolling_min(self._ts.low, self._period)
self._ts['period_high'] = period_high
self._ts['period_low'] = period_low
self._tlog = pf.TradeLog()
self._dbal = pf.DailyBal()
self._algo()
def run(self):
"""
Run the backtest.
Don't adjust the start day because that may cause it not
to match the start date of the strategy you are benchmarking
against. Instead, you should pass in the start date calculated
for the strategy.
"""
self.ts = pf.fetch_timeseries(self.symbol)
self.ts = pf.select_tradeperiod(self.ts,
self.start,
self.end,
use_adj=self.use_adj)
self.ts, _ = pf.finalize_timeseries(self.ts, self.start)
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
self._algo()
self._get_logs()
self._get_stats()
def run(self):
self.ts = pf.fetch_timeseries(self.symbol)
self.ts = pf.select_tradeperiod(self.ts,
self.start,
self.end,
use_adj=False)
# Add technical indicator: 200 sma regime filter
self.ts['regime'] = \
pf.CROSSOVER(self.ts, timeperiod_fast=1, timeperiod_slow=200)
# Add technical indicator: X day high, and X day low
period_high = pd.Series(self.ts.close).rolling(self.period).max()
period_low = pd.Series(self.ts.close).rolling(self.period).min()
self.ts['period_high'] = period_high
self.ts['period_low'] = period_low
self.ts, self.start = pf.finalize_timeseries(self.ts, self.start)
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
self._algo()
def run(self):
self.ts = pf.fetch_timeseries(self.symbol,
use_cache=self.options['use_cache'])
self.ts = pf.select_tradeperiod(self.ts, self.start, self.end,
self.options['use_adj'])
# Add technical indicator: day sma
self.ts['sma'] = SMA(self.ts, timeperiod=self.options['sma_period'])
# add S&P500 200 sma regime filter
ts = pf.fetch_timeseries('^GSPC')
ts = pf.select_tradeperiod(ts, self.start, self.end, use_adj=False)
self.ts['regime'] = \
pf.CROSSOVER(ts, timeperiod_fast=1, timeperiod_slow=200)
self.ts, self.start = pf.finalize_timeseries(self.ts, self.start)
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
self._algo()
self._get_logs()
self._get_stats()
sma1 = SMA(ts, timeperiod=2)
ts['sma1'] = sma1
sma5 = SMA(ts, timeperiod=5)
ts['sma5'] = sma5
fig = plt.figure()
axes = fig.add_subplot(111, ylabel='Price in $')
ts['close'].plot(ax=axes, label='close', color='k')
ts['sma1'].plot(ax=axes, label='sma1', color='r')
ts['sma5'].plot(ax=axes, label='sma5', color='b')
plt.legend(loc='best')
tlog = pf.TradeLog()
dbal = pf.DailyBal()
cash = capital
shares = 0
start_flag = True
t0 = time.time()
for i in range(len(ts.index)):
date = ts.index[i]
high = ts['high'][i]
low = ts['low'][i]
close = ts['close'][i]
sma1 = ts['sma1'][i]
sma5 = ts['sma5'][i]
