Freqtrade strategies

This Git repo contains free buy/sell strategies for Freqtrade >= 0.16.0.

Disclaimer

These strategies are for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.

Always start by testing strategies with a backtesting then run the trading bot in Dry-run. Do not engage money before you understand how it works and what profit/loss you should expect.

I strongly recommend you to have coding and Python knowledge. Do not hesitate to read the source code and understand the mechanism of this bot.

Table of Content

Free trading strategies
Contributes
FAQ
Offer me a coffee

Free trading strategies

Value below are result from backtesting from 2017-12-19 to 2017-01-20 and
experimental.sell_profit_only enabled. More detail on each strategy page.

Strategy	Buy count	AVG profit %	Total profit	AVG duration
Strategy 001	287	2.39	0.02763202	1306.3
Strategy 002	158	2.67	0.01686667	387.9
Strategy 003	147	2.21	0.01277113	694.9
Strategy 004	232	2.11	0.01977185	455.3

Strategies from this repo are free to use and feel free to update them. Most of them were designed from Hyperopt calculations.

Contributes

Feel free to send your comments, optimizations and requests via an Issue ticket.

Strategy requests

Are you looking to implement a new strategy, or one found on atrading Forum/Chan?
You can request it via Issue ticket. Please follow the template questions. Request that does not follow the template will be removed. I cannot promise to implement all of them, but will do my best to help.

FAQ

What is Freqtrade?

Freqtrade is a Simple High frequency trading bot for crypto currencies designed to support multi exchanges and be controlled via Telegram built by gcarq@.

This bot is similar other trading bot like Gekko, and Zenbot

What includes these strategies?

Each Strategies includes:

Minimal ROI: Minimal ROI optimized for the strategy.
Stoploss: Optimimal stoploss calculated based on hyperopt result.
Buy Strategy: Result from Hyperopt or based on exisiting trading strategies.
Sell Strategy
Indicators: Includes the indicators required to run the strategy.
Hyperopt configuration: To tune the strategy parameters.
Backtesting results

How were tested the strategies?

All strategies are tested with the dataset from this repo. The data set is located into user_data/data folder.
For each strategies, I run backtests for 2 Period and 2 parameters: experimental.sell_profit_only enabled and
experimental.sell_profit_only disabled

Period 1: From 2017-11-19 to 2017-12-20

experimental.sell_profit_only at true (Config file user_data/config-profit-on.json).
experimental.sell_profit_only at false (Config file user_data/config-profit-off.json).

Period 2: From 2017-12-19 to 2017-01-20

experimental.sell_profit_only at true (Config file user_data/config-profit-on.json).
experimental.sell_profit_only at false (Config file user_data/config-profit-off.json).

How to install a strategy?

First you need a working Freqtrade in version >= 0.16.0.

Note: This version is not merged yet but you can find into the branch feature/custom_strategy.

git clone https://github.com/gcarq/freqtrade.git
git checkout feature/custom_strategy

Once you have the bot on the right version, follow this steps:

Select the strategy you want. All strategies of the repo are into (user_data/strategies](https://github.com/glonlas/freqtrade-strategies/tree/feature/custom_strategy/user_data/strategies)
Copy the strategy file
Paste it into your user_data/strategies folder
Run the bot with the parameter -s <STRATEGY_FILE_NAME_WITHOUT_.py> (ex: python3 ./freqtrade/main.py -s strategy001)

How to test a strategy?

Let assume you have selected the strategy strategy-001.py:

Simple backtesting

python3 ./freqtrade/main.py -s strategy-001 backtesting --realistic-simulation

Refresh your test data

python3 ./freqtrade/main.py -s strategy-001 backtesting --realistic-simulation -r

Test with live data

python3 ./freqtrade/main.py -s strategy-001 backtesting --realistic-simulation -l

Which coins were tested?

You will find the list of coin tested into the configuration files (user_data/config-profit-on.json and user_data/config-profit-off.json)

Pair	Tested
BTC_ADA	Yes
BTC_NEO	Yes
BTC_NXT	Yes
BTC_MCO	Yes
BTC_ETH	Yes
BTC_BCC	Yes
BTC_VOX	Yes
BTC_GUP	Yes
BTC_SC	Yes
BTC_VTC	Yes
BTC_STRAT	Yes
BTC_OMG	Yes
BTC_OK	Yes
BTC_EDG	Yes
BTC_STORJ	Yes
BTC_EMC2	Yes
BTC_XLM	Yes
BTC_LSK	Yes
BTC_SYS	Yes
BTC_POWR	Yes
BTC_PAY	Yes
BTC_DGB	Yes
BTC_ETC	Yes
BTC_XRP	Yes
BTC_LTC	Yes
BTC_IOP	Yes
BTC_RCN	Yes
BTC_BTG	Yes
BTC_MONA	Yes
BTC_SALT	Yes
BTC_DASH	Yes
BTC_QTUM	Yes
BTC_CVC	Yes
BTC_KMD	Yes
BTC_XEM	Yes
BTC_XMR	Yes
BTC_ZEC	Yes
BTC_WAVES	Yes
BTC_PIVX	Yes
BTC_XZC	Yes
BTC_DOGE	No, this pair is blacklisted

Can I have your configuration file?

You will find them into user_data/ folder.

Can I have your datasets?

Yes of course! Datasets are into user_data/data folder. Download and use them.

How did you build dataset?

I am using data collected from Bittrex and run the script scripts/extract_data.py

python3 scripts/extract_data.py -f user_data/data/complete_data -d user_data/data/2017-11-19_2017-12-19 -s 2017-11-19 -e 2017-12-20
python3 scripts/extract_data.py -f user_data/data/complete_data -d user_data/data/2017-12-19_2018-01-19 -s 2017-12-19 -e 2018-01-20

Offer me a coffee

This repo is made for you to improve your trading strategies. If you are happy with the result of your strategy, feel free to offer me a coffee :)

BTC: 1KouEQdEKGiFGvm9iCb5K9pkUqnsASqmGS
ETH: 0x767D8AfB3B31131cBbf5b7318D2046996c9a40f2
LTC: LXFPwMs38DMj6ecD4xWEPnWjNAjp78uNZM

License

DevZenPro/freqtrade-strategies

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