alchemist_lib 0.1

Automatic trading library for cryptocurrencies.

---

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.

Supported Exchanges

The following exchanges are available to trade on:

• Poloniex

• Bittrex

Requirements

• Python3

• Mysql

Installation

Installing with pip:

If python3-pip is already installed:

$ pip3 install alchemist_lib

If you don’t have pip installed, you can easily install it by downloading and running get-pip.py.

Cloning the repository with git:

If git is already installed:

$ git clone https://github.com/Dodo33/alchemist-lib
$ cd alchemist-lib
$ python3 setup.py install

After the installation it’s important to specify mysql credentials:

$ sudo alchemist populate -l "hostname" -u "username" -p "password" -d "database_name"

Code example

The following strategy will analyse the trend for every cryptocurrency traded on Poloniex based on the slope of the linear regression. It buys the top 5 assets whose slope is closed to zero. The more the slope is close to zero, the more weight the asset gets within the portfolio. The procedure is repeated every 15 minutes.

from alchemist_lib.portfolio import LongsOnlyPortfolio
from alchemist_lib.broker import PoloniexBroker
from alchemist_lib.tradingsystem import TradingSystem
from alchemist_lib.factor import Factor
import alchemist_lib.exchange as exch


def set_weights(df):
    alphas_sum = df["alpha"].sum()

    for asset, alpha in zip(df.index.values, df["alpha"]):
        df.loc[asset, "tmp_weight"] = alphas_sum / alpha
    tmp_weight_sum = df["tmp_weight"].sum()

    for asset, tmp_weight in zip(df.index.values, df["tmp_weight"]):
        df.loc[asset, "weight"] = tmp_weight / tmp_weight_sum
    df.drop(labels = ["tmp_weight"], axis = 1, inplace = True)
    return df


def select_universe(session):
    return exch.get_assets(session = session, exchange_name = "poloniex")


def handle_data(session, universe):
    fct = Factor(session = session)
    prices = fct.history(universe = universe, field = "close", timeframe = "15M", window_length = 30)

    reg = fct.LinearRegression(values = prices, window_length = 30)
    df = reg.copy()
    df.rename(columns = {"LinearRegression" : "alpha"}, inplace = True)

    df["alpha"] = df["alpha"].abs()
    df.sort_values(by = ["alpha"], inplace = True)

    df.dropna(inplace = True)
    df = df.head(5)

    print("To invest in: ", df, "\n")
    return df


algo = TradingSystem(name = "AccumulationZone",
                     portfolio = LongsOnlyPortfolio(capital = 0.1),
                     set_weights = set_weights,
                     select_universe = select_universe,
                     handle_data = handle_data,
                     broker = PoloniexBroker(api_key = "APIKEY",
                                             secret_key = "SECRETKEY"),
                     test = True)
algo.run(delay = "15M", frequency = 1)

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.