Event-driven backtest/realtime quantitative trading system.

• Raw Data -> DataEvent

DataEvents process raw data and generate events. Raw data can be news, history price, market data, etc. A DataEvent is a numpy array and its first value must be a timestamp. It will be put into a priority queue with the timestamp so all history events for backtesting are ordered by time.

• DataEvent -> Signal (A value from -1 to 1)

Alphas represent trading strategies. An Alpha subscribes to a main_dataevent and has access to other DataEvents. It is triggered when its main_dataevent sends out a new data event. It processes data and calculates a signal which is sent to a handler in Portfolio.

• Signals -> Transactions

Portfolio is a combination of Alphas. It opens orders according to the signals received, and then sends them to Connectors. It also holds current positions and a list of Transactions.

• Transaction -> Execute

Connectors are order executors. A Connector could be an API wrapper of an exchange or a simulator using history data.

• Transactions -> Statistics

Statistics are triggered at the end of program. They will collect information from Transactions and save data, generate visual charts, calculate ratios, etc.

Transactions are records that store information through the pipeline. A Transaction is filled by different modules and stored in Portfolio.

EventQueue contains a priority queue and a locking system. DataEvents put events into the EventQueue and wait for its event to be consumed. There will be only one event in EventQueue for every DataEvent, so that all history events are triggered by time.

Manifest is a TOML file that defines the structure of a Portfolio and dependencies of modules.

git clone https://github.com/wynfred/presso.git cd presso sudo pip3 install . presso run example/manifest.toml # Press ENTER to stop DataEvents and run Statistics # Check presso.log for logs 

python3.6 aiohttp ccxt numpy toml 

from presso.core.abstract.alpha import AbstractAlpha class ExampleAlpha(AbstractAlpha): def _init(self): # TODO pass async def _calcSignal(self, data): # TODO pass @property def name(self): return 'Example' 

from presso.core.abstract.dataevent import AbstractDataEvent class ExampleDataEvent(AbstractDataEvent): def _init(self): # TODO pass async def _iter(self): # TODO pass 

from presso.core.abstract.portfolio import AbstractPortfolio class ExamplePortfolio(AbstractPortfolio): def _init(self): # TODO self._positions[TICKER.USD] = 100000 self._positions[TICKER.BTC] = 0 def onExampleSignal(self, transaction): # TODO if transaction.signal > 0 and self._positions[TICKER.USD] > 0: transaction.buy = TICKER.BTC transaction.sell = TICKER.USD transaction.total = self._positions[TICKER.USD] * 0.5 transaction.operation = OPERATION.MARKET self._execute(self._connectors['kline_history'], transaction) 

from presso.core.abstract.connector import AbstractConnector class ExampleConnector(AbstractConnector): def _init(self): # TODO pass async def execute(self, transaction): # TODO pass 

from presso.core.abstract.statistics import AbstractStatistics class ExampleStatistics(AbstractStatistics): def _init(self): # TODO pass def onTransaction(self, transaction): # TODO pass def finish(self): # TODO pass