This Git repo contains free buy/sell strategies for Freqtrade >= 0.16.0.
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.
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.
Feel free to send your comments, optimizations and requests via an Issue ticket.
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.
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
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
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
experimental.sell_profit_onlyattrue(Config file user_data/config-profit-on.json).experimental.sell_profit_onlyatfalse(Config file user_data/config-profit-off.json).
experimental.sell_profit_onlyattrue(Config file user_data/config-profit-on.json).experimental.sell_profit_onlyatfalse(Config file user_data/config-profit-off.json).
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_strategyOnce 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/strategiesfolder - Run the bot with the parameter
-s <STRATEGY_FILE_NAME_WITHOUT_.py>(ex:python3 ./freqtrade/main.py -s strategy001)
Let assume you have selected the strategy strategy-001.py:
Simple backtesting
python3 ./freqtrade/main.py -s strategy-001 backtesting --realistic-simulationRefresh your test data
python3 ./freqtrade/main.py -s strategy-001 backtesting --realistic-simulation -rTest with live data
python3 ./freqtrade/main.py -s strategy-001 backtesting --realistic-simulation -lYou 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 |
You will find them into user_data/ folder.
Yes of course! Datasets are into user_data/data folder. Download and use them.
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 2018-01-20This 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