Awesome

||spectre

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 alphalens and pyfolio

Python 3.7+, PyTorch 1.3+, Pandas 1.0+ recommended

Installation

pip install --no-deps git+git://github.com/Heerozh/spectre.git

Dependencies:

conda install pytorch torchvision torchaudio cudatoolkit=11.0 -c pytorch
conda install pyarrow pandas tqdm plotly requests

Benchmarks

My Machine：

• i9-7900X @ 3.30GHz, 20 Cores
• DDR4 3800MHz
• 3090: GIGABYTE GeForce RTX 3090 GAMING OC 24G
• 2080Ti: RTX 2080Ti Founders

Running on Quandl 5 years, 3196 Assets, total 3,637,344 bars.

• The CUDA memory used in the spectre benchmark is 1.8G, returned by cuda.max_memory_allocated().
• Benchmarks excluded the initial run (no copy data to VRAM, about saving 300ms).

Quick Start

DataLoader

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.

Factor and FactorEngine

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

Factor Analysis

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,))

Diagram

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.

Compatible with alphalens

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

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')

API

Note

Differences to zipline:

• In order to GPU optimize, the 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.
• spectre's normally using float32 data type 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 the align_by_time parameter of CsvDirLoader.

Differences to common chart:

• 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 'AdjustedColumnDataFactor(OHLCV.close)' as input data. This will speeds up a lot because it only needs to be adjusted once, but brings Look-Ahead Bias.
• Factors that uses the close data will be delayed by 1 bar.
• spectre's 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.
• spectre's RSI uses the algorithm same as zipline, for consistency in benchmarks.

Factors

Built-in Technical Indicator Factors list

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])

Factors Common Methods

# 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

Dataloader

CsvDirLoader

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})

ArrowLoader

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')

QuandlLoader

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')

How to write your own DataLoader

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 df

FactorEngine

A fast factor calculation pipeline.

FactorEngine.init

engine = FactorEngine(loader: DataLoader)

FactorEngine.add

engine.add(factor, column_name)

Add a factor to engine.

FactorEngine.set_filter

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.

FactorEngine.align_by_time

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.

FactorEngine.clear

engine.clear()

Remove global filter, and all factors.

FactorEngine.to_cuda

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.

FactorEngine.to_cpu

engine.to_cpu()

Switch to CPU mode.

FactorEngine.run

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.

*Auto Delay

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.

FactorEngine.plot_chart

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)' }
           }
})

FactorEngine.full_run

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.

FactorEngine.get_price_matrix

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.

FactorEngine.test_lookahead_bias

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.

ColumnDataFactor

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]]).

How to write your own factor

Inherit from factors.CustomFactor, write compute function.

All inputs will pass to compute function.

win = 1

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()

win > 1

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 ret

The 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)

Using Pandas Series

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.

Back-testing

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.

CustomAlgorithm.initialize

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.

CustomAlgorithm.terminate

alg.terminate(self, records: pd.DataFrame) Callback

Called when back-testing ends.

rebalance callback

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.

CustomAlgorithm.get_factor_engine

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.

CustomAlgorithm.create_factor_engine

self.create_factor_engine(name: str, loader: DataLoader = None) context: initialize

Create another engine, generally used when you need multiple data sources.

CustomAlgorithm.set_history_window

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.

CustomAlgorithm.schedule_rebalance

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.

CustomAlgorithm.schedule

self.schedule(event: Event) context: initialize

Schedule an event, callback is callback(source: "Any class who fired this event")

CustomAlgorithm.empty_cache_after_run

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.

CustomAlgorithm.stop_event_manager

alg.stop_event_manager() context: all

Stop backtesting or live trading.

CustomAlgorithm.fire_event

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)

CustomAlgorithm.results

self.results context: terminate

Get back-test results, same as the return value of trading.run_backtest

CustomAlgorithm.plot

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.

CustomAlgorithm.current

self.current context: rebalance

Current datetime, Read-Only.

CustomAlgorithm.get_price_matrix

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

CustomAlgorithm.record

self.record(**kwargs) context: rebalance

Record the data and pass all when calling terminate, use column = value format.

SimulationBlotter.set_commission

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)

SimulationBlotter.set_slippage

self.blotter.set_slippage(percentage=0, per_share=0.01) context: initialize, rebalance

Market impact add to the price.

SimulationBlotter.set_short_fee

self.blotter.set_short_fee(percentage=0) context: initialize

Set the transaction fees which only charged for sell orders.

SimulationBlotter.daily_curb

self.blotter.daily_curb = float context: initialize, rebalance

Limit on trading a specific asset if today to previous day return >= ±value. SLOW

SimulationBlotter.order_target

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.

SimulationBlotter.batch_order_target

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.

SimulationBlotter.order_target_percent

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.

SimulationBlotter.batch_order_target_percent

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.

SimulationBlotter.order

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.

SimulationBlotter.get_price

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')

SimulationBlotter.portfolio.set_stop_model

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()
        ...

PnLDecayTrailingStopModel

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.

TimeDecayTrailingStopModel

trading.TimeDecayTrailingStopModel(ratio, period_target: pd.Timedelta, callback, decay_rate=0.05, max_decay=0)

Same as PnLDecayTrailingStopModel, but target is time period.

SimulationBlotter.get_returns

self.blotter.get_returns() context: rebalance, terminate

Get the portfolio returns, use (self.blotter.get_returns() + 1).prod() to get current cumulative return.

SimulationBlotter.portfolio Read Only Properties

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 = None

self.blotter.portfolio.value Current portfolio value

self.blotter.portfolio.cash Current portfolio cash

self.blotter.portfolio.leverage Current portfolio leverage

spectre.trading.run_backtest

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 & Thanks

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.