StateOfTheArt-quant / sharpe

sharpe

sharpe is a unified, interactive, general-purpose environment for backtesting or applying machine learning(supervised learning and reinforcement learning) in the context of quantitative trading.

it's designed to allow maximum flexibility and simplicity in the practical research process: raw financial data -> feature engineering -> model training -> trading strategy -> backtesting, come in the reasonable level of abstraction.

core features:

• unified: unify the supervised learning and reinforcement learning in a framework.
• interactive: state(feature) -> action(portfolio-weight) -> reward(profit-and-loss/returns) bar-by-bar, allow maximum workflow control.
• general-purpose: market-independent, instrument-independent, trading-frequency-independent.

sharpe is considered in early alpha release. Stuff might break.

outline

• 1 Motivation and concept design
• 2 Install
• 3 Quick Start
• 4 Documentation
• 5 Examples
• 6 Communication & Contributing
• 7 Acknowledgements

1 Motivation and concept design

Before we walk through an end-to-end example how to backtest a trading stratey with sharpe, let’s take a step back and discuss and understand the difficuties encountering when design a backtest engine for quantitative trading, the answer derives from quantitative researchers own different types of trading philosophy, trade different types of instruments in different markets with different trading frequencies.

• different types of trading philosophy: rule-based methodology versus factor-based methodology(supervised learning versus reinforcement learning)
• different types of instruments in different market: stock, index, ETF, future in different countries and markets.
• different trading frequencies: intra-day trading(seconds, minutes, hours) and inter-day trading(daily, weekly, montly)

trading decison can be viewed as a special case of sequential decision-making, which can be formalized as follows: at each timestamp

• a agent sees a observation of the state of the environment, that the agent lives in and interacts with
• and then decides on an action to take, based on a policy(can be also called strategy, a mapping from state to action)
• The agent perceives a reward signal from the environment, a number that tells it how good or bad the current action is
• sees the observation of the next state, and iteratively

The goal of the agent is to find a good policy(strategy) to maximize its cumulative reward.

following this concept framwork, sharpe re-conceptualizes the process of trading and provides research with low-level, common tool to develop and backtest trading strategy.

2 Install

$ git clone https://github.com/StateOfTheArt-quant/sharpe
$ cd sharpe
$ python setup.py install

3 Quick Start

The following snippet showcases the whole workflow of trading strategy development in sharpe.

 from sharpe.utils.mock_data import create_toy_feature
 from sharpe.data.data_source import DataSource
 from sharpe.environment import TradingEnv
 from sharpe.mod.sys_account.api import order_target_weights
 import random
 random.seed(111)

 feature_df, price_s = create_toy_feature(order_book_ids_number=2, feature_number=3, start="2020-01-01", end="2020-01-11", random_seed=111)
 data_source = DataSource(feature_df=feature_df, price_s=price_s)

 env= TradingEnv(data_source=data_source, look_backward_window=4, mode="rl")
 print('----------------------------------------------------------------------')

 company_id = "000001.XSHE"


 def your_strategy(state):
      """
      here is a random strategy, only trade the first stock with a random target percent
      """

      target_percent_of_position =  round(random.random(),2)
      target_position_dict = {company_id : target_percent_of_position}
      print("the target portfolio is to be: {}".format(target_position_dict))
      # call trade API
      action = order_target_weights(target_position_dict)
      return action


 state = env.reset()

 while True:
      print("the current trading_dt is: {}".format(env.trading_dt))
      action = your_strategy(state)

      next_state, reward, done, info = env.step(action)
      print("the reward of this action: {}".format(reward))
      print("the next state is \n {}".format(next_state))
      if done:
           break
      else:
           state = next_state
env.render()

4 Documentation

• API documentation and an overview of the library: https://sharpe.readthedocs.io/en/latest/

5 Examples

• risk parity strategy

6 Communication & Contributing

• email: [email protected]

Working on your first Pull Request? You can learn how from this free series How to Contribute to an Open Source Project on GitHub

7 Acknowledgements

sharpe derived from our initial project trading_gym, which now is a event-driven(or observer) design pattern, the code highly inspired by RQALPHA

This library is named sharpe to respect William F. Sharpe