Flexible Trading Bot with main focus on Machine Learning and Genetic Algorithms, inspired by zenbot.
Mosquito is a cryptocurrency trading bot written in Python, with main focus on modularity, so it is straight forward to plug-in new exchange.
The idea to build a new bot came because of missing following easy-access features in all of available open-source bots:
- Multi-currency bot - Be able to monitor and exchange several currencies in one strategy (no need to run 5 same strategies for 5 different currencies).
- Easy AI plug & play - Possibility to easily plug any of the existing AI/ML libraries (for ex. scikit or keras)
Please be AWARE that Mosquito is still in beta and under heavy development. Please use it in Live trading VERY carefully.
Mosquito currently supports following exchanges:
- Poloniex - supporting fillOrKill and immediateOrCancel trading types. postOnly type is not supported. You can read more about trading types here.
- Bittrex - supporting Trade Limit Buy/Sell Orders
- Kucoin - work in progress
- Python 3.9
- mongodb
- Depending on the exchange you want to use:
- Clone repo
git clone https://github.com/miti0/mosquito.git
- Install requirements (ideally in separate virtual environment)
pip install -r requirements.txt
-
Install mongodb
-
Set-up mosquito.ini (if you want to use sample config, just rename mosquito.sample.ini to mosquito.ini)
-
Run desired command (full list of commands below)
All parameters in the program can be overridden with input arguments. You can get list of all available arguments with:
python mosquito.py --help
Backfill gets history data from exchange and stores them to mongodb. Data can be after that used for testing your simulation strategies.
usage: backfill.py [-h] [--pairs PAIRS] [--all] --days DAYS
optional arguments:
-h, --help show this help message and exit
--pairs PAIRS PairS to backfill. For ex. [BTC_ETH, BTC_* (to get all BTC_*
prefixed pairs]
--all Backfill data for ALL currencies
--days DAYS Number of days to backfill
Example 1) Load historical data for BTC_ETH pair for the last 5 days:
python backfill.py --days 5 --pairs USDT_BTC
Example 2) Load historical data for ALL pairs for the last 2 days
python backfill.py --days 3 --all
Example 3) Load historical data for all pairs starting with BTC_ for the last day
python backfill.py --days 1 --pairs BTC_*
This is the main module that handles passed strategy and places buy/sell orders.
Architecture and logic of mosquito is made so, that it should be easy to set and tune all strategy parameters with program arguments. Below is a list of main arguments that can be either configured via the mosquito.ini config file or by passing the value/values as argument.
-h, --help show this help message and exit
--polo_api_key POLO_API_KEY
Poloniex API key (default: None)
--polo_secret POLO_SECRET
Poloniex secret key (default: None)
--polo_txn_fee POLO_TXN_FEE
Poloniex txn. fee (default: None)
--polo_buy_order POLO_BUY_ORDER
Poloniex buy order type (default: None)
--polo_sell_order POLO_SELL_ORDER
Poloniex sell order type (default: None)
--bittrex_api_key BITTREX_API_KEY
Bittrex API key (default: None)
--bittrex_secret BITTREX_SECRET
Bittrex secret key (default: None)
--bittrex_txn_fee BITTREX_TXN_FEE
Bittrex txn. fee (default: None)
--exchange EXCHANGE Exchange (default: None)
--db_url DB_URL Mongo db url (default: None)
--db_port DB_PORT Mongo db port (default: None)
--db DB Mongo db (default: None)
--pairs PAIRS Pairs (default: None)
--use_real_wallet Use/not use fictive wallet (only for paper simulation)
(default: False)
--backtest_from BACKTEST_FROM
Backtest epoch start datetime (default: None)
--backtest_to BACKTEST_TO
Backtest epoch end datetime (default: None)
--backtest_days BACKTEST_DAYS
Number of history days the simulation should start
from (default: None)
--wallet_currency WALLET_CURRENCY
Wallet currency (separated by comma) (default: None)
--wallet_amount WALLET_AMOUNT
Wallet amount (separated by comma) (default: None)
--backtest Simulate your strategy on history ticker data
(default: False)
--paper Simulate your strategy on real ticker (default: False)
--live REAL trading mode (default: False)
--plot Generate a candle stick plot at simulation end
(default: False)
--ticker_size TICKER_SIZE Simulation ticker_size (default: 5)
--root_report_currency ROOT_REPORT_CURRENCY
Root currency used in final plot (default: None)
--buffer_size BUFFER_SIZE
Buffer size in days (default: 30)
--prefetch Prefetch data from history DB (default: False)
--plot_pair PLOT_PAIR
Plot pair (default: None)
--all ALL Include all currencies/tickers (default: None)
--days DAYS Days to pre-fill (default: None)
-c CONFIG, --config CONFIG
config file path (default: mosquito.ini)
-v, --verbosity Verbosity (default: False)
--strategy STRATEGY Strategy (default: None)
--fixed_trade_amount FIXED_TRADE_AMOUNT
Fixed trade amount (default: None)
Currently Trading supports following modes:
- Backtest - fast simulation mode using past data and placing fictive buy/sell orders.
- Paper - mode simulating live ticker with placing fictive buy/sell orders.
- Live - live trading with placing REAL buy/sell orders.
Backtest and Paper trading are using immediate buy/sell orders by using the last ticker closing price. This results to NOT 100% accurate strategy results, what you should be aware of.
Fast simulation mode using past data and placing fictive buy/sell orders. Simulation configuration is done via config.ini file (some of the parameters can be overridden with command line arguments).
Below is an example of running a backtest together with final buy/sell plot generated at the end of the simulation.
python3 mosquito.py --backtest --plot
! Please be aware that Backtest should 99% work, but it is currently under final verification test.
Trading mode that simulates live ticker with placing fictive buy/sell orders. Simulation configuration is done via config.ini file (some of the parameters can be overridden with command line arguments).
Below is an example of running a backtest together with final buy/sell plot generated at the end of the simulation.
python mosquito.py --paper
! Please be aware that Paper should 99% work, but it is currently under final verification test.
Live trading with placing REAL buy/sell orders. Configuration is done via config.ini file (some of the parameters can be overridden with command line arguments). Below is an example of running a backtest together with final buy/sell plot generated at the end of the simulation.
python mosquito.py --live
! Please be aware that Live should 99% work, but it is currently under final verification test.
Mosquito has a simple plot utility for visualizing current pair combined with trading history. Visualization uses external library plotly. Below You can see an example visualizing ticker price plot, together with simulated buy/sell orders.
Below is an example of Final Simulation Report summary:
****************************************************
Final simulation report:
****************************************************
Wallet at Start: | 50.0DGB |
Wallet at End: | 51.3464723121DGB |
Strategy result: -5.68%
Buy & Hold: -8.16%
Strategy vs Buy & Hold: 2.47%
Total txn: 10
Simulated (data time): 0 days, 4 hours and 55 minutes
Transactions per hour: 2.03
Simulation run time: 0 hours 1 minutes and 13 seconds
Blueprint is a part of AI package. Main function of the module is to generate datasets which can be used for training AI. Logic of Blueprint module is following:
-
Create a blueprint file/module which contains features, indicators and output parameters. As an example you can take a look at ai/blueprints/minimal.py or ai/blueprints/junior.py
-
Decide how many days you would like to run the Blueprint. Backfield data for that period.
-
Choose which pair/pairs you would like to include. Following combinations should work [BTC_ETH] - single pair, [BTC_ETH, BTC_LTC] - list of pairs, [BTC_*] - all pairs with prefix BTC
-
Start blueprint with following parameters (example below)
python blueprint.py --features junior --days 200
As a result you should see *.csv file in your Mosquito's out/blueprints folder, which should contain the dataset.
Simple module which sends up to 24h winners/losers market pairs summary by email in user specified intervals (sample below).
# You need to have configured email parameters in ini file, or pass them as input arguments.
python lense.py
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Copyright (C) 2023 (miro-ka)
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