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Merge pull request #25 from freqtrade/update_strategies

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Update strategies
This commit is contained in:
Misagh
2019-03-03 13:39:32 +01:00
committed by GitHub
parent 8b94b72d21 0ff6cb1d89
commit df348f0f77
6 changed files with 156 additions and 31 deletions
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README.md
+12 -7
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@@ -1,6 +1,6 @@
# Freqtrade strategies
This Git repo contains free buy/sell strategies for [Freqtrade](https://github.com/freqtrade/freqtrade) >= `0.16.0`.
This Git repo contains free buy/sell strategies for [Freqtrade](https://github.com/freqtrade/freqtrade).
## Disclaimer
@@ -31,6 +31,7 @@ bot.
- [How to create/optimize a strategy?](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md)
## Free trading strategies
Value below are result from backtesting from 2018-01-10 to 2018-01-30 and
`experimental.sell_profit_only` enabled. More detail on each strategy
page.
@@ -47,13 +48,18 @@ page.
Strategies from this repo are free to use. Feel free to update them.
Most of them were designed from Hyperopt calculations.
Some only work in specific market conditions, while others are more "general purpose" strategies.
It's noteworthy that depending on the exchange and Pairs used, further optimization can bring better results.
## Share your own strategies and contribute to this repo
Feel free to send your strategies, comments, optimizations and pull requests via an
[Issue ticket](https://github.com/freqtrade/freqtrade-strategies/issues/new).
## FAQ
### What is Freqtrade?
[Freqtrade](https://github.com/freqtrade) is a Simple High
frequency trading bot for crypto currencies designed to support
@@ -80,8 +86,7 @@ enabled and disabled.
### How to install a strategy?
First you need a [working Freqtrade](https://github.com/freqtrade/freqtrade/blob/develop/docs/index.md)
in version >= 0.16.0.
First you need a [working Freqtrade](https://freqtrade.io).
Once you have the bot on the right version, follow this steps:
@@ -91,6 +96,8 @@ Once you have the bot on the right version, follow this steps:
3. Paste it into your `user_data/strategies` folder
4. Run the bot with the parameter `-s <STRATEGY CLASS NAME>` (ex: `python3 ./freqtrade/main.py -s Strategy001`)
[More information](https://www.freqtrade.io/en/latest/bot-optimization/)
### How to test a strategy?
Let assume you have selected the strategy `strategy001.py`:
@@ -107,12 +114,10 @@ python3 ./freqtrade/main.py -s Strategy001 backtesting
python3 ./freqtrade/main.py -s Strategy001 backtesting --refresh-pairs-cached
```
*Note:* Generally, it's recommendet to use static backtest data (from a defined period of time) for compareable results.
#### Test with live data
```bash
python3 ./freqtrade/main.py -s Strategy001 backtesting --live
```
## Can I have your configuration file?
You will find them into [user_data/](https://github.com/freqtrade/freqtrade-strategies/tree/master/user_data) folder.
user_data/strategies/strategy001.py → user_data/strategies/Strategy001.py
+29 -5
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@@ -13,7 +13,7 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
# Update this variable if you change the class name
class strategy001(IStrategy):
class Strategy001(IStrategy):
"""
Strategy 001
author@: Gerald Lonlas
@@ -34,12 +34,36 @@ class strategy001(IStrategy):
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.3
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# run "populate_indicators" only for new candle
ta_on_candle = 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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
@@ -58,7 +82,7 @@ class strategy001(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> 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
@@ -74,7 +98,7 @@ class strategy001(IStrategy):
return dataframe
def populate_sell_trend(self, dataframe: DataFrame) -> 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
user_data/strategies/strategy002.py → user_data/strategies/Strategy002.py
+29 -5
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@@ -10,7 +10,7 @@ import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class strategy002(IStrategy):
class Strategy002(IStrategy):
"""
Strategy 002
author@: Gerald Lonlas
@@ -31,12 +31,36 @@ class strategy002(IStrategy):
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.3
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# run "populate_indicators" only for new candle
ta_on_candle = 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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
@@ -68,7 +92,7 @@ class strategy002(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> 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
@@ -85,7 +109,7 @@ class strategy002(IStrategy):
return dataframe
def populate_sell_trend(self, dataframe: DataFrame) -> 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
user_data/strategies/strategy003.py → user_data/strategies/Strategy003.py
+29 -5
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@@ -10,7 +10,7 @@ import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class strategy003(IStrategy):
class Strategy003(IStrategy):
"""
Strategy 003
author@: Gerald Lonlas
@@ -31,12 +31,36 @@ class strategy003(IStrategy):
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.3
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# run "populate_indicators" only for new candle
ta_on_candle = 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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
@@ -78,7 +102,7 @@ class strategy003(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> 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
@@ -102,7 +126,7 @@ class strategy003(IStrategy):
return dataframe
def populate_sell_trend(self, dataframe: DataFrame) -> 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
user_data/strategies/strategy004.py → user_data/strategies/Strategy004.py
+29 -5
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@@ -8,7 +8,7 @@ from pandas import DataFrame
import talib.abstract as ta
class strategy004(IStrategy):
class Strategy004(IStrategy):
"""
Strategy 004
@@ -30,12 +30,36 @@ class strategy004(IStrategy):
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.3
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# run "populate_indicators" only for new candle
ta_on_candle = 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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
@@ -72,7 +96,7 @@ class strategy004(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> 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
@@ -102,7 +126,7 @@ class strategy004(IStrategy):
return dataframe
def populate_sell_trend(self, dataframe: DataFrame) -> 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
user_data/strategies/strategy005.py → user_data/strategies/Strategy005.py
+28 -4
View File
@@ -33,12 +33,36 @@ class Strategy005(IStrategy):
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.5
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# run "populate_indicators" only for new candle
ta_on_candle = 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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
@@ -80,7 +104,7 @@ class Strategy005(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> 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
@@ -102,7 +126,7 @@ class Strategy005(IStrategy):
return dataframe
def populate_sell_trend(self, dataframe: DataFrame) -> 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