Alchemist_lib is an automatic trading library for cryptocurrencies that allow to personalize the portfolio based on a specific strategy.
- Easy to use: The interface is similar to zipline, a popular backtesting software for stocks.
- Portfolio personalization: You can choose the weight of every element on the portfolio.
- Most common technical analysis indicators already integrated.
- Execute orders on the most famous exchanges.
- Possibility to visualize the asset allocation and the portfolio value charts for every strategy thanks to alchemist-view.
- Fully documented and hosted on readthedocs.
The following exchanges are available to trade on:
- Python3
- Mysql
See the installing documentation.
Strategy description: Hold a portfolio equally composed by Ethereum and BitcoinCash.
from alchemist_lib.portfolio import LongsOnlyPortfolio
from alchemist_lib.broker import PoloniexBroker
from alchemist_lib.tradingsystem import TradingSystem
import alchemist_lib.exchange as exch
import pandas as pd
def set_weights(df):
df["weight"] = 0.5 #Because there are just two assets.
return df
def select_universe(session):
poloniex_assets = exch.get_assets(session = session, exchange_name = "poloniex")
my_universe = []
for asset in poloniex_assets:
if asset.ticker == "ETH" or asset.ticker == "BCH":
my_universe.append(asset)
return my_universe
def handle_data(session, universe):
#The value of alpha is useless in this case.
df = pd.DataFrame(data = {"asset" : universe, "alpha" : 0}, columns = ["asset", "alpha"]).set_index("asset")
return df
algo = TradingSystem(name = "BuyAndHold",
portfolio = LongsOnlyPortfolio(capital = 0.02),
set_weights = set_weights,
select_universe = select_universe,
handle_data = handle_data,
broker = PoloniexBroker(api_key = "APIKEY",
secret_key = "SECRETKEY"),
paper_trading = True)
algo.run(delay = "15M", frequency = 1)
Alchemist_lib works with three methods:
- set_weights
- select_universe
- handle_data
set_weights is used to set the weight that an asset has respect the others within the portfolio. The sum of every weight must be close to 1. Must returns a pandas dataframe with two columns: "asset" and "alpha", where "asset" is the index.
select_universe filters the assets saved on the database and returns just the ones the strategy will take into consideration.
handle_data is the most importat one because it manages the trading logic. Must returns a pandas dataframe with two columns: "asset" and "alpha", where "asset" is the index.
You can find other examples in the examples directory.
A bug tracker is provided by Github.