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touch config files to dockerize

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This commit is contained in:
kecheon 2021-04-13 18:42:13 +09:00
parent 75c89bcd64
commit bef088ff90
4 changed files with 283 additions and 7 deletions
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8
docker-compose.yml
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@ -1,5 +1,5 @@
---
version: '3'
version: "3"
services:
freqtrade:
image: freqtradeorg/freqtrade:stable
@ -17,12 +17,12 @@ services:
# Expose api on port 8080 (localhost only)
# Please read the https://www.freqtrade.io/en/latest/rest-api/ documentation
# before enabling this.
# ports:
# - "127.0.0.1:8080:8080"
ports:
- "127.0.0.1:8080:8080"
# Default command used when running `docker compose up`
command: >
trade
--logfile /freqtrade/user_data/logs/freqtrade.log
--db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
--config /freqtrade/user_data/config.json
--strategy SampleStrategy
--strategy Strategy002

6
tests/strategy/strats/default_strategy.py
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@ -29,7 +29,7 @@ class DefaultStrategy(IStrategy):
stoploss = -0.10
# Optimal timeframe for the strategy
timeframe = '5m'
timeframe = '15m'
# Optional order type mapping
order_types = {
@ -119,7 +119,7 @@ class DefaultStrategy(IStrategy):
"""
dataframe.loc[
(
(dataframe['rsi'] < 35) &
(dataframe['rsi'] < 30) &
(dataframe['fastd'] < 35) &
(dataframe['adx'] > 30) &
(dataframe['plus_di'] > 0.5)
@ -142,7 +142,7 @@ class DefaultStrategy(IStrategy):
dataframe.loc[
(
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) |
(qtpylib.crossed_above(dataframe['rsi'], 75)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
) &
(dataframe['adx'] > 10) &

141
user_data/config.json Normal file
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@ -0,0 +1,141 @@
{
"max_open_trades": 2,
"stake_currency": "BTC",
"stake_amount": "unlimited",
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
"timeframe": "1m",
"dry_run": true,
"dry_run_wallet": 1,
"cancel_open_orders_on_exit": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30
},
"bid_strategy": {
"price_side": "bid",
"ask_last_balance": 0.0,
"use_order_book": false,
"order_book_top": 1,
"check_depth_of_market": {
"enabled": false,
"bids_to_ask_delta": 1
}
},
"ask_strategy": {
"price_side": "ask",
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 1,
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false
},
"exchange": {
"name": "binance",
"key": "",
"secret": "",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 200
},
"pair_whitelist": [
// "ADA/BTC",
"ALGO/BTC",
// "ATOM/BTC",
"BAT/BTC",
// "BCH/BTC",
"BRD/BTC",
"BTG/BTC",
// "CAKE/BTC",
// "CHZ/BTC",
// "DCR/BTC",
"DOGE/BTC",
// "DREP/BTC",
"DASH/BTC",
// "EGLD/BTC",
// "EOS/BTC",
"ENJ/BTC",
// "ETH/BTC",
// "FTT/BTC",
"GTO/BTC",
"HBAR/BTC",
"IOST/BTC",
"IOTA/BTC",
// "LINK/BTC",
"LTC/BTC",
"MANA/BTC",
// "MITH/BTC",
"MKR/BTC",
// "NEO/BTC",
// "NKN/BTC",
// "NXS/BTC",
// "ONT/BTC",
"PPT/BTC",
// "QTUM/BTC",
// "RUNE/BTC",
"RVN/BTC",
// "SOL/BTC",
"SRM/BTC",
"TFUEL/BTC",
"THETA/BTC",
"TRX/BTC",
"VET/BTC",
"VIA/BTC",
"WAVES/BTC",
// "XEM/BTC",
"XLM/BTC",
"XMR/BTC",
"XRP/BTC",
"XTZ/BTC",
// "ZIL/BTC",
"ZRX/BTC",
"SXP/BTC",
// "WAN/BTC",
// "WIN/BTC",
],
"pair_blacklist": [
"BNB/BTC"
]
},
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "1356047596:AAHuyBLkOuoJ0SSYYOt5TXd2YBUzD3uzoEA",
"chat_id": "bitsbee1_bot"
},
"api_server": {
"enabled": true,
"listen_ip_address": "0.0.0.0",
"listen_port": 8080,
"verbosity": "error",
"enable_openapi": false,
"jwt_secret_key": "06907b5231babf844792af80070386aab331065690b15f7d86964666fc64fc30",
"CORS_origins": [],
"username": "kecheon@gmail.com",
"password": "dmdcjs0"
},
"bot_name": "freqtrade",
"initial_state": "running",
"forcebuy_enable": false,
"internals": {
"process_throttle_secs": 5
}
}

135
user_data/strategies/Strategy002.py Normal file
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@ -0,0 +1,135 @@
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class Strategy002(IStrategy):
"""
Strategy 002
author@: Gerald Lonlas
github@: https://github.com/freqtrade/freqtrade-strategies
How to use it?
> python3 ./freqtrade/main.py -s Strategy002
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"60": 0.01,
"30": 0.03,
"20": 0.04,
"0": 0.05
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.10
# Optimal timeframe for the strategy
timeframe = '1m'
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
# run "populate_indicators" only for new candle
process_only_new_candles = False
# Experimental settings (configuration will overide these if set)
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
"""
return [
("ETH/USDT", "15m"),
("BTC/USDT", "5m"),
]
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = stoch['slowk']
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['rsi'] < 30) &
(dataframe['slowk'] < 20) &
(dataframe['bb_lowerband'] > dataframe['close']) &
(dataframe['CDLHAMMER'] == 100)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['sar'] > dataframe['close']) &
(dataframe['fisher_rsi'] > 0.55)
),
'sell'] = 1
return dataframe