spectre is a GPU-accelerated Parallel quantitative trading library, focused on performance.
- Fast GPU Factor Engine, see below Benchmarks
- Pure python code, based on PyTorch, so it can integrate DL model very smoothly.
- Compatible with
alphalensandpyfolio
Python 3.7, pandas 0.25 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：
- i9-7900X @ 3.30GHz, 20 Cores
- DDR4 3800MHz
- RTX 2080Ti Founders
Running on Quandl 5 years, 3196 Assets, total 3,637,344 bars.
| spectre (CUDA) | spectre (CPU) | zipline.pipeline | |
|---|---|---|---|
| SMA(100) | 126 ms ± 759 µs (23.7x) | 2.68 s ± 36.1 ms (1.11x) | 2.98 s ± 14.4 ms (1x) |
| EMA(50) win=200 | 179 ms ± 692 µs (42.5x) | 4.37 s ± 46.4 ms (1.74x) | 7.6 s ± 15.4 ms (1x) |
| (MACD+RSI+STOCHF).rank.zscore | 473 ms ± 73.2 ms (30.2x) | 6.01 s ± 28.1 (2.38x) | 14.3 s ± 277 ms (1x) |
- The CUDA memory used in the spectre benchmark is 0.9G, returned by cuda.max_memory_allocated().
- Benchmarks excluded the initial run (no copy data to VRAM, about saving 300ms).
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
from spectre.data import ArrowLoader
loader = ArrowLoader('./prices/yahoo/yahoo.feather')
engine = factors.FactorEngine(loader)
universe = factors.AverageDollarVolume(win=120).top(100)
engine.set_filter( universe )
ma_cross = (factors.MA(5)-factors.MA(10)-factors.MA(30))
bb_cross = -factors.BBANDS(win=5)
bb_cross = bb_cross.filter(bb_cross < 0.7) # p-hacking
ma_cross_factor = ma_cross.rank(mask=universe).zscore()
bb_cross_factor = bb_cross.rank(mask=universe).zscore()
engine.add( ma_cross_factor, 'ma_cross' )
engine.add( bb_cross_factor, 'bb_cross' )
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:
bb_cross_factor.show_graph()
The thickness of the line represents the length of the Rolling Window, kind of like "bandwidth".
The engine in GPU mode performs multiple inputs calculations simultaneously, the two branch paths of the orange RankFactor in the diagram above will be calculated simultaneously, the same goes for NormalizedBollingerBands.
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
class MyAlg(trading.CustomAlgorithm):
def initialize(self):
# setup engine
engine = self.get_factor_engine()
engine.to_cuda()
universe = factors.AverageDollarVolume(win=120).top(100)
engine.set_filter( universe )
# add your factors
bb_cross = -factors.BBANDS(win=5)
bb_cross = bb_cross.filter(bb_cross < 0.7) # p-hacking
bb_cross_factor = bb_cross.rank(mask=universe).zscore()
engine.add( bb_cross_factor.to_weight(), 'bb_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.bb_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', 'bb_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-', secondary_y=True)
loader = ArrowLoader('./prices/yahoo/yahoo.feather')
%time results = trading.run_backtest(loader, MyAlg, '2013-01-01', '2018-01-01')
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')- In order to GPU optimize, the
CustomFactor.computefunction 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 usingengine.test_lookahead_biasdo some tests. - spectre's
QuandlLoaderusing float32 datatype for GPU performance. - spectre FactorEngine arranges data by bars, so
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 thealign_by_timeparameter ofCsvDirLoader.
- If there is adjustments data, the prices is re-adjusted every day, so the factor you got, like MA, will be different from the stock chart software which only adjusted according to last day. If you want adjusted by last day, use like 'AdjustedDataFactor(OHLCV.close)' as input data. This will speeds up a lot because it only needs to be adjusted once, but brings Look-Ahead Bias.
loader = spectre.data.CsvDirLoader(prices_path: str, prices_by_year=False, earliest_date: pd.Timestamp = None, dividends_path=None, splits_path=None, 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.
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.set_align_by_time(True)
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()
Switch to GPU mode.
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.
delay_factor=True means that the results will shift(1), in theory you can't trade immediately
when you get the factor data. If you only use 'Open' prices, you can set it to False.
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: DataFactor = 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.
# All technical factors passed comparison test with TA-Lib
Returns(inputs=[OHLCV.close])
LogReturns(inputs=[OHLCV.close])
SimpleMovingAverage = MA = SMA(win=5, inputs=[OHLCV.close])
VWAP(inputs=[OHLCV.close, OHLCV.volume])
ExponentialWeightedMovingAverage = EMA(win=5, inputs=[OHLCV.close])
AverageDollarVolume(win=5, inputs=[OHLCV.close, OHLCV.volume])
AnnualizedVolatility(win=20, inputs=[Returns(win=2), 252])
NormalizedBollingerBands = 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
# To filter (Comparison operator):
new_filter = (factor1 < factor2) | (factor1 > 0)
# 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) # filter only this factor
# Multiple returns selecting
new_factor = factor[0]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
class LogReturns(factors.CustomFactor):
inputs = [factors.Returns(factors.OHLCV.close)]
win = 1
def compute(self, change: torch.Tensor) -> torch.Tensor:
return change.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.
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")
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.
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')
context: rebalance, terminate
self.blotter.portfolio.positions Current position, Dict[asset, shares] type.
self.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.
A spectre is haunting Market — the spectre of capitalism.