spectre is a GPU-accelerated Parallel quantitative trading library, focused on performance.
alphalens and pyfolioPython 3.7+, PyTorch 1.3+, Pandas 1.0+ recommended
pip install --no-deps git+git://github.com/Heerozh/spectre.gitDependencies:
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
conda install pyarrow pandas tqdm plotly requestsMy Machine：
Running on Quandl 5 years, 3196 Assets, total 3,637,344 bars.
| spectre (CUDA) | spectre (CPU) | zipline.pipeline | |
|---|---|---|---|
| SMA(100) | 144 ms ± 974 µs (20.7x) | 2.68 s ± 36.1 ms (1.11x) | 2.98 s ± 14.4 ms (1x) |
| EMA(50) win=229 | 270 ms ± 1.89 ms (31.0x) | 4.37 s ± 46.4 ms (1.74x) | 8.38 s ± 56.8 ms (1x) |
| (MACD+RSI+STOCHF).rank.zscore | 282 ms ± 1.33 ms (50.7x) | 6.01 s ± 28.1 (2.38x) | 14.3 s ± 277 ms (1x) |
First of all is data, you can use CsvDirLoader read your csv files.
spectre also has built-in Yahoo downloader, symbols=None will download all SP500 components.
from spectre.data import YahooDownloader
YahooDownloader.ingest(start_date="2001", save_to="./prices/yahoo", symbols=None, skip_exists=True)You can use spectre.data.ArrowLoader('./prices/yahoo/yahoo.feather') load those data now.
from spectre import factors
from spectre.data import ArrowLoader
loader = ArrowLoader('./prices/yahoo/yahoo.feather')
engine = factors.FactorEngine(loader)
engine.to_cuda()
engine.add(factors.SMA(5), 'ma5')
engine.add(factors.OHLCV.close, 'close')
df = engine.run('2019-01-11', '2019-01-15')
df| ma5 | close | ||
|---|---|---|---|
| date | asset | ||
| 2019-01-14 00:00:00+00:00 | A | 68.842003 | 70.379997 |
| AAPL | 151.615997 | 152.289993 | |
| ABC | 75.835999 | 76.559998 | |
| ABT | 69.056000 | 69.330002 | |
| ADBE | 234.537994 | 237.550003 | |
| ... | ... | ... | ... |
| 2019-01-15 00:00:00+00:00 | XYL | 68.322006 | 69.160004 |
| YUM | 91.010002 | 90.000000 | |
| ZBH | 102.932007 | 102.690002 | |
| ZION | 43.760002 | 44.320000 | |
| ZTS | 85.846001 | 84.500000 |
from spectre import factors
import math
risk_free_rate = 0.04 / 252
excess_logret = factors.LogReturns() - math.log(1 + risk_free_rate)
universe = factors.AverageDollarVolume(win=120).top(100)
# Barra MOMENTUM
ema126 = factors.EMA(half_life=126, inputs=[excess_logret])
rstr = ema126.shift(11).sum(252)
MOMENTUM = rstr
# Barra Volatility
ema42 = factors.EMA(half_life=42, inputs=[excess_logret])
dastd = factors.STDDEV(252, inputs=[ema42])
VOLATILITY = dastd
# run engine
from spectre.data import ArrowLoader
loader = ArrowLoader('./prices/yahoo/yahoo.feather')
engine = factors.FactorEngine(loader)
engine.set_filter( universe )
engine.add( MOMENTUM, 'MOMENTUM' )
engine.add( VOLATILITY, 'VOLATILITY' )
engine.to_cuda()
%time factor_data, mean_return = engine.full_run("2013-01-02", "2018-01-19", periods=(1,5,10,))
You can also view your factor structure graphically:
factors.BBANDS(win=5).normalized().rank().zscore().show_graph()
The thickness of the line represents the length of the Rolling Window, kind of like "bandwidth".
If engine.to_cuda(enable_stream=True), the calculation of the branches will be performed
simultaneously, but the VRAM usage will increase proportionally.
The return value of full_run is compatible with alphalens:
import alphalens as al
...
factor_data, _ = engine.full_run("2013-01-02", "2018-01-19")
clean_data = factor_data[['{factor_name}', 'Returns']].droplevel(0, axis=1)
al.tears.create_full_tear_sheet(clean_data)Back-testing uses FactorEngine's results as data, market event as triggers.
You can find other examples in the ./examples directory.
from spectre import factors, trading
from spectre.data import ArrowLoader
import pandas as pd, math
class MyAlg(trading.CustomAlgorithm):
def initialize(self):
# your factors
risk_free_rate = 0.04 / 252
excess_logret = factors.LogReturns() - math.log(1 + risk_free_rate)
universe = factors.AverageDollarVolume(win=120).top(100)
# Barra MOMENTUM Risk Factor
ema126 = factors.EMA(half_life=126, inputs=[excess_logret])
rstr = ema126.shift(11).sum(252)
MOMENTUM = rstr.zscore(mask=universe)
# Barra Volatility Risk Factor
ema42 = factors.EMA(half_life=42, inputs=[excess_logret])
dastd = factors.STDDEV(252, inputs=[ema42])
VOLATILITY = dastd.zscore(mask=universe)
# setup engine
engine = self.get_factor_engine()
engine.to_cuda()
engine.set_filter( universe )
engine.add( (MOMENTUM + VOLATILITY).to_weight(), 'alpha_weight' )
# schedule rebalance before market close
self.schedule_rebalance(trading.event.MarketClose(self.rebalance, offset_ns=-10000))
# simulation parameters
self.blotter.capital_base = 1000000
self.blotter.set_commission(percentage=0, per_share=0.005, minimum=1)
# self.blotter.set_slippage(percentage=0, per_share=0.4)
def rebalance(self, data: 'pd.DataFrame', history: 'pd.DataFrame'):
data = data.fillna(0)
self.blotter.batch_order_target_percent(data.index, data.alpha_weight)
# closing asset position that are no longer in our universe.
removes = self.blotter.portfolio.positions.keys() - set(data.index)
self.blotter.batch_order_target_percent(removes, [0] * len(removes))
# record data for debugging / plotting
self.record(aapl_weight=data.loc['AAPL', 'alpha_weight'],
aapl_price=self.blotter.get_price('AAPL'))
def terminate(self, records: 'pd.DataFrame'):
# plotting results
self.plot(benchmark='SPY')
# plotting the relationship between AAPL price and weight
ax1 = records.aapl_price.plot()
ax2 = ax1.twinx()
records.aapl_weight.plot(ax=ax2, style='g-')
loader = ArrowLoader('./prices/yahoo/yahoo.feather')
%time results = trading.run_backtest(loader, MyAlg, '2014-01-01', '2019-01-01')
It awful but you get the idea.
The return value of run_backtest is compatible with pyfolio:
import pyfolio as pf
pf.create_full_tear_sheet(results.returns, positions=results.positions.value, transactions=results.transactions,
live_start_date='2017-01-03')CustomFactor.compute function calculates the results of all bars
at once, so you need to be careful to prevent Look-Ahead Bias, because the inputs are not just
historical data. Also using engine.test_lookahead_bias do some tests.Return(win=10) means 10 bars return, may
actually be more than 10 days if some assets not open trading in period. You can change this
behavior by aligning data: filling missing bars with NaNs in your DataLoader, please refer to the
align_by_time parameter of CsvDirLoader.EMA uses the algorithm same as zipline and Dataframe.ewm(span=...), when span is
greater than 100, it will be slightly different from common EMA.RSI uses the algorithm same as zipline, for consistency in benchmarks. Returns(inputs=[OHLCV.close])
LogReturns(inputs=[OHLCV.close])
SimpleMovingAverage = MA = SMA(win=5, inputs=[OHLCV.close])
VWAP(inputs=[OHLCV.close, OHLCV.volume])
ExponentialWeightedMovingAverage = EMA(span=5, inputs=[OHLCV.close])
AverageDollarVolume(win=5, inputs=[OHLCV.close, OHLCV.volume])
AnnualizedVolatility(win=20, inputs=[Returns(win=2), 252])
BollingerBands = BBANDS(win=20, inputs=[OHLCV.close, 2])
MovingAverageConvergenceDivergenceSignal = MACD(12, 26, 9, inputs=[OHLCV.close])
TrueRange = TRANGE(inputs=[OHLCV.high, OHLCV.low, OHLCV.close])
RSI(win=14, inputs=[OHLCV.close])
FastStochasticOscillator = STOCHF(win=14, inputs=[OHLCV.high, OHLCV.low, OHLCV.close])
StandardDeviation = STDDEV(win=5, inputs=[OHLCV.close])
RollingHigh = MAX(win=5, inputs=[OHLCV.close])
RollingLow = MIN(win=5, inputs=[OHLCV.close])# Standardization
new_factor = factor.rank(mask=filter)
new_factor = factor.demean(mask=filter, groupby: 'dict or column_name'=None)
new_factor = factor.zscore(mask=filter)
new_factor = factor.to_weight(mask=filter, demean=True) # return a weight that sum(abs(weight)) = 1
# Quick computation
new_factor = factor1 + factor1
new_factor = factor.abs()
new_factor = factor.sum()
# To filter (Comparison operator):
new_filter = (factor1 < factor2) | (factor1 > 0)
new_filter[n_features] = factor.one_hot() # one-hot encoding
new_filter = factor.any(win=5)
new_filter = factor.all(win=5)
# Rank filter
new_filter = factor.top(n)
new_filter = factor.bottom(n)
# Specific assets
new_filter = StaticAssets({'AAPL', 'MSFT'})
# Local filter
new_factor = factor.filter(some_filter) # fills elements of self with NaN where mask is False
# Multiple returns selecting
new_factor = factor[0]
# Others
new_factor = factor.shift(1)
new_factor = factor.quantile(bins=5) # factor value quantile groupby datetime
new_factor = factor.fill_na(0)
new_factor = factor.fill_na(ffill=True) # propagate last valid observation forward to next valid
loader = spectre.data.CsvDirLoader(prices_path: str, prices_by_year=False, earliest_date: pd.Timestamp = None, dividends_path=None, splits_path=None, file_pattern='*.csv', calender_asset: str = None, align_by_time=False, ohlcv=('open', 'high', 'low', 'close', 'volume'), adjustments=None, split_ratio_is_inverse=False, split_ratio_is_fraction=False, prices_index='date', dividends_index='exDate', splits_index='exDate', **read_csv)
Read CSV files in the directory, each file represents an asset.
Reading csv is very slow, so you also need to use ArrowLoader.
prices_path: Prices csv folder. When encountering duplicate datetime in prices_index,
Loader will keep the last, drop others.\
prices_index: index_colfor csv in prices_path\
prices_by_year: If prices file name like 'spy_2017.csv', set this to True\
ohlcv: Required, OHLCV column names. When you don't need to use adjustments and
factors.OHLCV, you can set this to None.\
adjustments: Optional, list, dividend amount and splits ratio column names.\
dividends_path: Dividends csv folder, structured as one csv per asset.
For duplicate data, loader will first drop the exact same rows, and then for the same
dividends_index but different 'dividend amount(adjustments[0])' rows, loader will sum them up.
If dividends_path not set, the adjustments[0] column is considered to be included
in the prices csv.\
dividends_index: index_colfor csv in dividends_path.\
splits_path: Splits csv folder, structured as one csv per asset.
When encountering duplicate datetime in splits_index, Loader will use the last
non-NaN 'split ratio', drop others.
If splits_path not set, the adjustments[1] column is considered to be included
in the prices csv.\
splits_index: index_colfor csv in splits_path.\
split_ratio_is_inverse: If split ratio calculated by to/from, set to True.
For example, 2-for-1 split, to/form = 2, 1-for-15 Reverse Split, to/form = 0.6666...\
split_ratio_is_fraction: If split ratio in csv is fraction string, like 1/3, set to True.\
file_pattern: csv file name pattern, default is '*.csv'.\
earliest_date: Data before this date will not be read, save memory.\
calender_asset: Asset name as trading calendar, like 'SPY', for clean up non-trading
time data.\
align_by_time: If True and calender_asset is not None, the index of datetime will be the same
for all assets, if some assets have no data at that time, NaNs will be filled. The benefit is
that the columns of data matrix in CustomFactor.compute will also be aligned.\
**read_csv: Parameters for all csv when calling pd.read_csv.
parse_dates or date_parser is required.
Example for load IEX CSV files:
usecols = {'date', 'uOpen', 'uHigh', 'uLow', 'uClose', 'uVolume', 'exDate', 'amount', 'ratio'}
csv_loader = spectre.data.CsvDirLoader(
'./iex/daily/', calender_asset='SPY',
dividends_path='./iex/dividends/',
splits_path='./iex/splits/',
ohlcv=('uOpen', 'uHigh', 'uLow', 'uClose', 'uVolume'), adjustments=('amount', 'ratio'),
prices_index='date', dividends_index='exDate', splits_index='exDate',
parse_dates=True, usecols=lambda x: x in usecols,
dtype={'uOpen': np.float32, 'uHigh': np.float32, 'uLow': np.float32, 'uClose': np.float32,
'uVolume': np.float64, 'amount': np.float64, 'ratio': np.float64})Ingest data from other DataLoader into a feather file, speed up reading speed a lot.
3GB data takes about 7 seconds on initial load.
Ingest
spectre.data.ArrowLoader.ingest(source=CsvDirLoader(...), save_to='./filename.feather')
Read
loader = spectre.data.ArrowLoader('./filename.feather')
no longer updated, only contain prices before 2018
Download 'WIKI_PRICES.zip' (You need an account):
https://www.quandl.com/api/v3/datatables/WIKI/PRICES.csv?qopts.export=true&api_key=[yourapi_key]
from spectre.data import ArrowLoader, QuandlLoader
ArrowLoader.ingest(source=QuandlLoader('WIKI_PRICES.zip'),
save_to='wiki_prices.feather')Inherit from DataLoader, overriding the _load method, read data into a large DataFrame,
index is MultiIndex ['date', 'asset'], where date is Datetime type, asset is string type,
and then call self._format(df, split_ratio_is_inverse) to format the data.
Also call test_load in your test case to do basic format testing.
For example, suppose you have a csv file that contains data for all assets:
class YourLoader(spectre.data.DataLoader):
@property
def last_modified(self) -> float:
return os.path.getmtime(self._path)
def __init__(self, file: str, calender_asset='SPY') -> None:
super().__init__(file,
ohlcv=('open', 'high', 'low', 'close', 'volume'),
adjustments=('ex-dividend', 'split_ratio'))
self._calender = calender_asset
def _load(self) -> pd.DataFrame:
df = pd.read_csv(self._path, parse_dates=['date'],
usecols=['asset', 'date', 'open', 'high', 'low', 'close',
'volume', 'ex-dividend', 'split_ratio', ],
dtype={
'open': np.float32, 'high': np.float32, 'low': np.float32,
'close': np.float32, 'volume': np.float64,
'ex-dividend': np.float64, 'split_ratio': np.float64
})
df.set_index(['date', 'asset'], inplace=True)
df = self._format(df, split_ratio_is_inverse=True)
if self._calender:
df = self._align_to(df, self._calender)
return dfA fast factor calculation pipeline.
engine = FactorEngine(loader: DataLoader)
engine.add(factor, column_name)
Add a factor to engine.
engine.set_filter(factor: FilterFactor or None)
Set the Global Filter, engine deletes rows which Global Filter returns as False at the last step, affect all factors.
engine.align_by_time = bool
Same as CsvDirLoader(align_by_time=True), but it's dynamic. Notes: Very slow on large amounts of
data, and if the data source is already aligned, this method cannot make it return to unaligned.
engine.clear()
Remove global filter, and all factors.
engine.to_cuda(enable_stream=False)
Switch to GPU mode.
Set enable_stream to True allows pipeline branches to calculation simultaneously. However, this will lead to more VRAM usage and may also affect performance.
engine.to_cpu()
Switch to CPU mode.
df = engine.run(start_time, end_time, delay_factor=True)
Run the engine to calculate the factor data, return a DataFrame. The column is each added factor.
By default, delay_factor is True, it means enable auto-delay. If 'high, low, close, volume' data
is used by a terminal factor (including its upstream), that factor will be delayed by shift(1)
in the last step, because in theory you can't trade on this factor before it generated. Others
will not be delayed, in order to provide the latest data as much as possible.
Set to False to force engine not delay any factors.
engine.plot_chart(start_time, end_time, trace_types=None, styles=None, delay_factor=True)
Plotting common stock price chart for researching.
trace_types: dict(factor_name=plotly_trace_type), trace type can be 'Bar', or 'Scatter',
default is 'Scatter'.
styles: dict(factor_name=plotly_trace_styles), add the trace styles, please refer
to plotly documentation: Scatter traces
rsi = factors.RSI()
buy_signal = (rsi.shift(1) < 30) & (rsi > 30)
engine = factors.FactorEngine(loader)
engine.timezone = 'America/New_York'
engine.set_filter(factors.StaticAssets({'NVDA', 'MSFT'}))
engine.add(factors.MA(20), 'MA20')
engine.add(rsi, 'RSI')
engine.add(factors.OHLCV.close.filter(buy_signal), 'Buy')
engine.to_cuda()
_ = engine.plot_chart('2017', '2018', styles={
'MA20': {
'line': {'dash': 'dash'}
},
'RSI': {
'yaxis': 'y3', # y1: price axis, y2: volume axis, yN: add new y-axis
'line': {'width': 1}
},
'Buy': {
'mode': 'markers',
'marker': { 'symbol': 'triangle-up', 'size': 10, 'color': 'rgba(0, 0, 255, 0.5)' }
}
})
factor_data, mean_returns = engine.full_run( start_time, end_time, trade_at='close', periods=(1, 4, 9), quantiles=5, filter_zscore=20, demean=True, preview=True)
Not only run the engine, but also run factor analysis.
df_prices = engine.get_price_matrix(start_time, end_time, prices: ColumnDataFactor = OHLCV.close)
Get the adjusted historical prices matrix which columns is all assets.
If global filter is setted, all unfiltered assets from start_time to end_time will be included.
engine.test_lookahead_bias(start_time, end_time)
Run the engine to test if there is a lookahead bias.
Fill random values to second half of the ohlcv data, and then check if there are differences between the two runs in the first half.
You can use ColumnDataFactor to represents data from any column in the DataLoader, for example:
spectre.factors.ColumnDataFactor(inputs=['col_name'])
factors.OHLCV.close is just a sugar way to write
spectre.factors.ColumnDataFactor (inputs = [data_loader.ohlcv[3]]).
Inherit from factors.CustomFactor, write compute function.
All inputs will pass to compute function.
When win = 1, the inputs data is tensor type, the first dimension of data is the asset, the
second dimension is each bar price data. Note that if the data is align_by_time=False, the number
of bars for each asset is different and not aligned (for example, the time for each price in bar_t3
column may be inconsistent).
+-----------------------------------+
| bar_t1 bar_t3 |
| | | |
| v v |
| asset 1--> [[1.1, 1.2, 1.3, ...], |
| asset 2--> [ 5, 6, 7, ...]] |
+-----------------------------------+Example of LogReturns:
from spectre import factors
import torch
class LogReturns(factors.CustomFactor):
inputs = [factors.Returns(2, inputs=[factors.OHLCV.close])]
win = 1
def compute(self, change: torch.Tensor) -> torch.Tensor:
return (change + 1).log()If rolling window is required(win > 1), all inputs data will be wrapped into
spectre.parallel.Rolling.
This is just an unfolded tensor data, but because the data is very large after unfolded, for
better performance and saving VRAM, the rolling class automatically splits the data into multiple
small chunks. You need to use the agg method to operating tensor.
from spectre import factors, parallel
class OvernightReturn(factors.CustomFactor):
inputs = [factors.OHLCV.open, factors.OHLCV.close]
win = 2
def compute(self, opens: parallel.Rolling, closes: parallel.Rolling) -> torch.Tensor:
ret = opens.last() / closes.first() - 1
return retThe closes.first() above is just a helper method for closes.agg(lambda x: x[:, :, 0]),
where x[:, :, 0] return the first element of rolling window. The first dimension of x is the
asset, the second dimension is each bar, and the third dimension is the bar price and historical
price with win length, and Rolling.agg runs on all the chunks and combines them.
+------------------win=3-------------------+
| history_t-2 curr_bar_value |
| | | |
| v v |
| asset 1-->[[[nan, nan, 1.1], <--bar_t1 |
| [nan, 1.1, 1.2], <--bar_t2 |
| [1.1, 1.2, 1.3]], <--bar_t3 |
| |
| asset 2--> [[nan, nan, 5], <--bar_t1 |
| [nan, 5, 6], <--bar_t2 |
| [ 5, 6, 7]]] <--bar_t3 |
+------------------------------------------+Rolling.agg can carry multiple Rolling objects, such as
weighted_mean = lambda _close, _volume: (_close * _volume).sum(dim=2) / _volume.sum(dim=2)
close.agg(weighted_mean, volume)CustomFactor's inputs data is a matrix without DataFrame's Index information. If you need index, or not familiar with PyTorch, here is a another way:
from spectre import factors
class YourFactor(factors.CustomFactor):
def compute(self, data: torch.Tensor) -> torch.Tensor:
# convert to pd.Series data
pd_series = self._revert_to_series(data)
# ...
# convert back to grouped tensor
return self._regroup(pd_series)This method is completely non-parallel and inefficient, but easy to write.
Quick Start contains easy-to-understand examples, please read first.
The spectre.trading.CustomAlgorithm currently does not supports live trading,
will implement it in the future.
alg.initialize(self) Callback
Called when back-testing starts, at least you need use get_factor_engine to add factors
and call schedule_rebalance here.
alg.terminate(self, records: pd.DataFrame) Callback
Called when back-testing ends.
rebalance(self, data: pd.DataFrame, history: pd.DataFrame) Callback
The function name does not have to be 'rebalance', it can be specified in schedule_rebalance.
data is the factors data of last bar returned by FactorEngine;
history same as data, but contains previous data, please refer to set_history_window.
Put calculations into the FactorEngine as much as possible can improve backtest performance.
self.get_factor_engine(name: str = None)
context: initialize, rebalance, terminate
Get the factor engine of this trading algorithm. But note that you can add factors or filter only
during initialize, otherwise it will cause unexpected effects.
The algorithm has a default engine, name can be None.
But if you created multiple engines using create_factor_engine, you need to specify which one.
self.create_factor_engine(name: str, loader: DataLoader = None)
context: initialize
Create another engine, generally used when you need multiple data sources.
self.set_history_window(offset: pd.DateOffset=None)
context: initialize
Set the length of historical data passed to each rebalance call. SLOW
Default: If None, pass all available historical data, so there will be no historical data on the first day, one historical row on the next day, and so on.
self.schedule_rebalance(event: Event)
context: initialize
Schedule rebalance to be called when an event occurs.
Events are: MarketOpen, MarketClose, EveryBarData,
For example:
alg.schedule_rebalance(trading.event.MarketClose(self.any_function))The Market* events has offset_ns parameter MarketClose(self.any_function, offset_ns=-1000),
a negative value of offset_ns means 'before', in backtest mode, the magnitude of the value has no
effect.
self.schedule(event: Event)
context: initialize
Schedule an event, callback is callback(source: "Any class who fired this event")
self.empty_cache_after_run = True
context: initialize
Empty engine's cache after factor calculation. If you need more VRMA in rebalance context, or wanna play 3D game when backtesting, set it to True will help.
alg.stop_event_manager()
context: all
Stop backtesting or live trading.
alg.fire_event(event_type: Type[Event])
context: all
Trigger a type of event (any subclasses that inherit from Event）,
for example: alg.fire_event(MarketClose), (do not do this, do not fire built-in events)
self.results
context: terminate
Get back-test results, same as the return value of trading.run_backtest
self.plot(annual_risk_free=0.04, benchmark: Union[pd.Series, str] = None)
context: terminate
Plot a simple portfolio cumulative return chart.\
benchmark: pd.Series of benchmark daily return, or an asset name.
self.current
context: rebalance
Current datetime, Read-Only.
self.get_price_matrix(length: pd.DateOffset, name: str = None, prices=OHLCV.close)
context: rebalance
Help method for calling engine.get_price_matrix, name specifies which engine.
Returns the historical asset prices, adjusted and filtered by the current time. Slow
self.record(**kwargs)
context: rebalance
Record the data and pass all when calling terminate, use column = value format.
self.blotter.set_commission(percentage=0, per_share=0.005, minimum=1)
context: initialize
percentage: percentage part, calculated by percentage * price * shares\
per_share: calculated by per_share * shares\
minimum: minimum commission if above sum does not exceed
commission = max(percentage_part + per_share_part, minimum)
self.blotter.set_slippage(percentage=0, per_share=0.01)
context: initialize, rebalance
Market impact add to the price.
self.blotter.set_short_fee(percentage=0)
context: initialize
Set the transaction fees which only charged for sell orders.
self.blotter.daily_curb = float
context: initialize, rebalance
Limit on trading a specific asset if today to previous day return >= ±value. SLOW
self.blotter.order_target(asset: str, target: number)
context: rebalance
Place an order on an asset to target number of shares in position, negative number means short.
If asset cannot be traded or limited by daily_curb, it will return False.
self.blotter.batch_order_target(asset: Iterable[str], target: Iterable[float])
context: rebalance
Same as SimulationBlotter.order_target, but for multiple assets.
Return value is a list of skipped assets, which indicate that they cannot be traded or limited by
daily_curb.
self.blotter.order_target_percent(asset: str, pct: float)
context: rebalance
Place an order on an asset to target percentage of portfolio net value, negative number means short.
If asset cannot be traded or limited by daily_curb, it will return False.
self.blotter.batch_order_target_percent(asset: Iterable[str], pct: Iterable[float])
context: rebalance
Same as SimulationBlotter.order_target_percent, but for multiple assets and better performance.
Return value is a list of skipped assets, which indicate that they cannot be traded or limited by
daily_curb.
self.blotter.order(asset: str, amount: int)
context: rebalance
Order a certain amount of an asset, negative number means short.
If asset cannot be traded or limited by daily_curb, it will return False.
float = self.blotter.get_price(asset: Union[str, Iterable])
context: rebalance
Get current price of assert.
Notice: Batch calls are slow, You can add prices as factor to get the price,
like: engine.add(OHLCV.close, 'prices')
self.blotter.portfolio.set_stop_model(model: StopModel)
context: initialize
Set stop tracking model for positions, models are:
trading.StopModel(ratio, callback)\
trading.TrailingStopModel(ratio, callback)\
trading.PnLDecayTrailingStopModel and trading.TimeDecayTrailingStopModel
Stop loss example:
class Backtester(trading.CustomAlgorithm):
def initialize(self):
...
self.blotter.portfolio.set_stop_model(trading.TrailingStopModel(-0.1, self.stop))
def stop(self, asset, amount):
self.blotter.order(asset, amount)
self.record(...)
def rebalance(self, data, history):
self.blotter.portfolio.check_stop_trigger()
...trading.PnLDecayTrailingStopModel(ratio, pnl_target, callback, decay_rate=0.05, max_decay=0)
This is a model that can stop gain and stop loss at the same time.
Exponential decay to the stop ratio: ratio * decay_rate ^ (PnL% / PnL_target%),
So PnLDecayTrailingStopModel(-0.1, 0.1, callback) means initial stop loss is -10%, and the
ratio will decrease when profit% approaches the target +10%. If recorded high profit% exceeds 10%,
any drawdown will trigger a stop loss.
trading.TimeDecayTrailingStopModel(ratio, period_target: pd.Timedelta, callback, decay_rate=0.05, max_decay=0)
Same as PnLDecayTrailingStopModel, but target is time period.
self.blotter.get_returns()
context: rebalance, terminate
Get the portfolio returns, use (self.blotter.get_returns() + 1).prod() to get current cumulative
return.
context: rebalance, terminate
self.blotter.portfolio.positions Current positions, Dict[asset, Position] type.
class Position:
shares = None
average_price = None
last_price = None
unrealized = None
realized = Noneself.blotter.portfolio.value Current portfolio value
self.blotter.portfolio.cash Current portfolio cash
self.blotter.portfolio.leverage Current portfolio leverage
results = trading.run_backtest(loader: DataLoader, alg_type: Type[CustomAlgorithm], start, end)
Run backtest, return value is namedtuple:
results.returns: daily return
results.positions: daily positions
results.transactions: full transactions with all orders
Copyright (C) 2019-2020, by Zhang Jianhao (heeroz@gmail.com), All rights reserved.
Thanks to JetBrains's support.
A spectre is haunting Market — the spectre of capitalism.