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Description:

alchemist lib 1.0

Alchemist_lib

Description
Alchemist_lib is an automatic trading library for cryptocurrencies that allow to personalize the portfolio based on a specific strategy.

Features

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.

Supported Exchanges
The following exchanges are available to trade on:

Poloniex
Bittrex

Requirements

Python3
Mysql

Installation
See the installing documentation.

Code example
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)

Screenshot

Basic concepts
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.

Reporting bugs
A bug tracker is provided by Github.