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.
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.
Contents¶
Install¶
Installing requirements¶
Lets install python3 and mysql.
GNU/Linux¶
First of all to make sure that everything is up to date, let’s update and upgrade the system with apt-get.
$ sudo apt-get update
$ sudo apt-get -y upgrade
Probably python3 is already installed so let’s check.
$ python3 -V
If the command above returns something like Python 3.5.2 it’s all ok. Otherwise install python with the following command.
$ sudo apt-get install python3
To manage software packages for Python, let’s install pip.
$ sudo apt-get install python3-pip
A more detailed guide can be found on Digital Ocean.
Installing MySQL can be done by runnung the following command.
$ sudo apt-get install mysql-server
The MySQL/Python connector can be installed with:
$ sudo apt-get install python3-mysql.connector
Installing alchemist_lib¶
Installing with pip:¶
If python3-pip is already installed:
$ sudo pip3 install alchemist_lib
$ sudo pip3 install git+https://github.com/femtotrader/pandas_talib.git
$ sudo pip3 install https://github.com/s4w3d0ff/python-poloniex/archive/v0.4.7.zip
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
$ sudo pip3 install git+https://github.com/femtotrader/pandas_talib.git
$ sudo pip3 install https://github.com/s4w3d0ff/python-poloniex/archive/v0.4.7.zip
Important¶
After the installation it’s important to specify mysql credentials:
$ sudo alchemist populate -l "hostname" -u "username" -p "password" -d "database_name"
Beginner Tutorial¶
Basics¶
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. The df parameter is the dataframe returned by handle_data.
Must returns a pandas dataframe with two columns: “asset” and “weight”, where “asset” is the index.
select_universe have to returns a list of assets the strategy will take into consideration.
If you want all the assets traded on a specific exchange just call the get_assets function of alchemist_lib.exchange.
handle_data is the most importat one because it manages the trading logic. The universe parameter is the list returned by select_universe.
Must returns a pandas dataframe with two columns: “asset” and “alpha”, where “asset” is the index.
To start the strategy you just need to instantiate the TradingSystem class and call the run method.
- Note:
- Remember to test the strategy with real-time data before going live, it can be done setting
paper_trading = True.
First strategy¶
Lets take a look at a very simple strategy from the examples directory, buyandhold.py.
- Strategy description:
- Hold a portfolio equally composed by Ethereum and BitcoinCash.
First of all we must import all the things we need.
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
Then we select which assets we want to buy and hold. Just ETH and BCH in this example:
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
In this case the handle_data method is useless so lets set a random value for the “alpha” column of the dataframe.
def handle_data(session, universe):
df = pd.DataFrame(data = {"asset" : universe, "alpha" : 0}, columns = ["asset", "alpha"]).set_index("asset")
return df
We want to hold two assets (ETH and BCH) so every one must be 50% of the portfolio value.
def set_weights(df):
df["weight"] = 0.5
return df
Make it starts in paper trading mode, every 4 hours.
algo = TradingSystem(name = "BuyAndHold",
portfolio = LongsOnlyPortfolio(capital = 0.01),
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 = "4H", frequency = 1)
Example¶
Another example, a little bit more complex is emacrossover.py.
- Strategy description:
- Hold a portfolio composed by top 5 assets by volume whose EMA 10 is above the EMA 21. Rebalance it every hour.
Code:
from alchemist_lib.portfolio import LongsOnlyPortfolio
from alchemist_lib.broker import BittrexBroker
from alchemist_lib.tradingsystem import TradingSystem
from alchemist_lib.factor import Factor
import pandas as pd
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, "weight"] = alpha / alphas_sum
return df
def select_universe(session):
return exch.get_assets(session = session, exchange_name = "bittrex")
def handle_data(session, universe):
fct = Factor(session = session)
prices = fct.history(universe = universe, field = "close", timeframe = "1H", window_length = 21)
ema10 = fct.ExponentialMovingAverage(values = prices, window_length = 10, field = "close").rename(columns = {"ExponentialMovingAverage" : "ema10"})
ema21 = fct.ExponentialMovingAverage(values = prices, window_length = 21, field = "close").rename(columns = {"ExponentialMovingAverage" : "ema21"})
concated = pd.concat([ema10, ema21], axis = 1)
concated = concated.loc[concated["ma10"] > concated["ma21"], :]
vol = fct.history(universe = concated.index.values, field = "volume", timeframe = "1H", window_length = 1)
df = pd.concat([concated, vol], axis = 1)
df = df[["volume"]].rename(columns = {"volume" : "alpha"})
if len(df) > 5:
df = df.sort_values(by = "volume", ascending = False)
df = df.head(5)
return df
algo = TradingSystem(name = "MovingAverageCrossover",
portfolio = LongsOnlyPortfolio(capital = 0.1),
set_weights = set_weights,
select_universe = select_universe,
handle_data = handle_data,
broker = BittrexBroker(api_key = "APIKEY",
secret_key = "SECRETKEY"),
paper_trading = True)
algo.run(delay = "1H", frequency = 1)
To execute it:
$ python3 emacrossover.py
Conclusion¶
These were some basic examples of how alchemist_lib works.
Take a look at the example folder for more examples.
License¶
MIT License
Copyright (c) 2018 Carniel Giorgio
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.