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docker run -d --name freqtrade -v ${PWD}/config.json:/freqtrade/config.json -v ${PWD}/tradesv3.sqlite:/freqtrade/tradesv3.sqlite -v ${PWD}/user_data/:/freqtrade/user_data/ freqtrade backtesting --strategy-list Strategy001 Strategy002 Strategy005

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*For requestion a new strategy. Please use the template below.*
*Any strategy request that does not follow the template will be closed.*
## Step 1: What indicators are required?
*Please list all the indicators required for the buy and sell strategy.*
## Step 2: Explain the Buy Strategy
*Please explain in details the indicators you need to run the buy strategy, then
explain in detail what is the trigger to buy.*
## Step 1: Explain the Sell Strategy
*Please explain in details the indicators you need to run the sell strategy, then
explain in detail what is the trigger to sell.*
## Source
What come from this strategy? Cite your source:
* http://

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Thank you for sending your pull request.
## Summary
Explain in one sentence the goal of this PR / Strategy
Solve the issue: #___
## Quick strategy idea
- <change log #1>
- <change log #2>

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PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.

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# Freqtrade strategies
This Git repo contains free buy/sell strategies for [Freqtrade](https://github.com/freqtrade/freqtrade).
## 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.
We 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](#free-trading-strategies)
- [Contribute](#share-your-own-strategies-and-contribute-to-this-repo)
- [FAQ](#faq)
- [What is Freqtrade?](#what-is-freqtrade)
- [What includes these strategies?](#what-includes-these-strategies)
- [How to install a strategy?](#how-to-install-a-strategy)
- [How to test a strategy?](#how-to-test-a-strategy)
- [How to create/optimize a strategy?](https://www.freqtrade.io/en/latest/strategy-customization/)
## Free trading strategies
Value below are result from backtesting from 2018-01-10 to 2018-01-30 and
`ask_strategy.sell_profit_only` enabled. More detail on each strategy
page.
| Strategy | Buy count | AVG profit % | Total profit | AVG duration | Backtest period |
|-----------|-----------|--------------|--------------|--------------|-----------------|
| [Strategy 001](https://github.com/freqtrade/freqtrade-strategies/blob/master/user_data/strategies/Strategy001.py) | 55 | 0.05 | 0.00012102 | 476.1 | 2018-01-10 to 2018-01-30 |
| [Strategy 002](https://github.com/freqtrade/freqtrade-strategies/blob/master/user_data/strategies/Strategy002.py) | 9 | 3.21 | 0.00114807 | 189.4 | 2018-01-10 to 2018-01-30 |
| [Strategy 003](https://github.com/freqtrade/freqtrade-strategies/blob/master/user_data/strategies/Strategy003.py) | 14 | 1.47 | 0.00081740 | 227.5 | 2018-01-10 to 2018-01-30 |
| [Strategy 004](https://github.com/freqtrade/freqtrade-strategies/blob/master/user_data/strategies/Strategy004.py) | 37 | 0.69 | 0.00102128 | 367.3 | 2018-01-10 to 2018-01-30 |
| [Strategy 005](https://github.com/freqtrade/freqtrade-strategies/blob/master/user_data/strategies/Strategy005.py) | 180 | 1.16 | 0.00827589 | 156.2 | 2018-01-10 to 2018-01-30 |
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.
Please keep in mind, results will heavily depend on the pairs, timeframe and timerange used to backtest - so please run your own backtests that mirror your usecase, to evaluate each strategy for yourself.
## 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) or as a [Pull request](https://github.com/freqtrade/freqtrade-strategies/pulls) enhancing this repository.
## FAQ
### What is Freqtrade?
[Freqtrade](https://github.com/freqtrade/freqtrade) Freqtrade is a free and open source crypto trading bot written in Python.
It is designed to support all major exchanges and be controlled via Telegram. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
### What includes these strategies?
Each Strategies includes:
- [x] **Minimal ROI**: Minimal ROI optimized for the strategy.
- [x] **Stoploss**: Optimimal stoploss.
- [x] **Buy signals**: Result from Hyperopt or based on exisiting trading strategies.
- [x] **Sell signals**: Result from Hyperopt or based on exisiting trading strategies.
- [x] **Indicators**: Includes the indicators required to run the strategy.
Best backtest multiple strategies with the exchange and pairs you're interrested in, and finetune the strategy to the markets you're trading.
### How to install a strategy?
First you need a [working Freqtrade](https://freqtrade.io).
Once you have the bot on the right version, follow this steps:
1. Select the strategy you want. All strategies of the repo are into
[user_data/strategies](https://github.com/freqtrade/freqtrade/tree/develop/user_data/strategies)
2. Copy the strategy file
3. Paste it into your `user_data/strategies` folder
4. Run the bot with the parameter `--strategy <STRATEGY CLASS NAME>` (ex: `freqtrade trade --strategy Strategy001`)
More information [about backtesting](https://www.freqtrade.io/en/latest/backtesting/) and [strategy customization](https://www.freqtrade.io/en/latest/strategy-customization/).
### How to test a strategy?
Let assume you have selected the strategy `strategy001.py`:
#### Simple backtesting
```bash
freqtrade backtesting --strategy Strategy001
```
#### Refresh your test data
```bash
freqtrade download-data --days 100
```
*Note:* Generally, it's recommended to use static backtest data (from a defined period of time) for comparable results.
Please check out the [official backtesting documentation](https://www.freqtrade.io/en/latest/backtesting/) for more information.

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import talib.abstract as ta
from pandas import DataFrame
from typing import Dict, Any, Callable, List
from functools import reduce
from skopt.space import Categorical, Dimension, Integer, Real
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
shortRangeBegin = 10
shortRangeEnd = 20
mediumRangeBegin = 100
mediumRangeEnd = 120
class AverageHyperopt(IHyperOpt):
"""
Hyperopt file for optimizing AverageStrategy.
Uses ranges of EMA periods to find the best parameter combination.
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
for short in range(shortRangeBegin, shortRangeEnd):
dataframe[f'maShort({short})'] = ta.EMA(dataframe, timeperiod=short)
for medium in range(mediumRangeBegin, mediumRangeEnd):
dataframe[f'maMedium({medium})'] = ta.EMA(dataframe, timeperiod=medium)
return dataframe
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by hyperopt
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use
"""
conditions = []
# TRIGGERS
if 'trigger' in params:
conditions.append(qtpylib.crossed_above(
dataframe[f"maShort({params['trigger'][0]})"],
dataframe[f"maMedium({params['trigger'][1]})"])
)
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
buyTriggerList = []
for short in range(shortRangeBegin, shortRangeEnd):
for medium in range(mediumRangeBegin, mediumRangeEnd):
# The output will be (short, long)
buyTriggerList.append(
(short, medium)
)
return [
Categorical(buyTriggerList, name='trigger')
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by hyperopt
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use
"""
# print(params)
conditions = []
# TRIGGERS
if 'sell-trigger' in params:
conditions.append(qtpylib.crossed_above(
dataframe[f"maMedium({params['sell-trigger'][1]})"],
dataframe[f"maShort({params['sell-trigger'][0]})"])
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters
"""
sellTriggerList = []
for short in range(shortRangeBegin, shortRangeEnd):
for medium in range(mediumRangeBegin, mediumRangeEnd):
# The output will be (short, long)
sellTriggerList.append(
(short, medium)
)
return [
Categorical(sellTriggerList, name='sell-trigger')
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of from strategy
must align to populate_indicators in this file
Only used when --spaces does not include buy
"""
dataframe.loc[
(
qtpylib.crossed_above(
dataframe[f'maShort({shortRangeBegin})'],
dataframe[f'maMedium({mediumRangeBegin})'])
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of from strategy
must align to populate_indicators in this file
Only used when --spaces does not include sell
"""
dataframe.loc[
(
qtpylib.crossed_above(
dataframe[f'maMedium({mediumRangeBegin})'],
dataframe[f'maShort({shortRangeBegin})'])
),
'sell'] = 1
return dataframe

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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import talib.abstract as ta
from pandas import DataFrame
from typing import Dict, Any, Callable, List
# import numpy as np
from skopt.space import Categorical, Dimension, Integer, Real
# import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class_name = 'MACDStrategy_hyperopt'
# This class is a sample. Feel free to customize it.
class MACDStrategy_hyperopt(IHyperOpt):
"""
This is an Example hyperopt to inspire you. - corresponding to MACDStrategy in this repository.
To run this, best use the following command (adjust to your environment if needed):
```
freqtrade hyperopt --strategy MACDStrategy --hyperopt MACDStrategy_hyperopt --spaces buy sell
```
The idea is to optimize only the CCI value.
- Buy side: CCI between -700 and 0
- Sell side: CCI between 0 and 700
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['cci'] = ta.CCI(dataframe)
return dataframe
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by hyperopt
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use
"""
dataframe.loc[
(
(dataframe['macd'] > dataframe['macdsignal']) &
(dataframe['cci'] <= params['buy-cci-value']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(-700, 0, name='buy-cci-value'),
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by hyperopt
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use
"""
dataframe.loc[
(
(dataframe['macd'] < dataframe['macdsignal']) &
(dataframe['cci'] >= params['sell-cci-value'])
),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters
"""
return [
Integer(0, 700, name='sell-cci-value'),
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of from strategy
must align to populate_indicators in this file
Only used when --spaces does not include buy
"""
dataframe.loc[
(
(dataframe['macd'] > dataframe['macdsignal']) &
(dataframe['cci'] <= -50.0)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of from strategy
must align to populate_indicators in this file
Only used when --spaces does not include sell
"""
dataframe.loc[
(
(dataframe['macd'] < dataframe['macdsignal']) &
(dataframe['cci'] >= 100.0)
),
'sell'] = 1
return dataframe

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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from functools import reduce
from typing import Any, Callable, Dict, List
import talib.abstract as ta
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class ReinforcedSmoothScalp(IHyperOpt):
"""
Default hyperopt provided by the Freqtrade bot.
You can override it with your own Hyperopt
"""
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
# if 'rsi-enabled' in params and params['rsi-enabled']:
# conditions.append(dataframe['rsi'] < params['rsi-value'])
if 'fastk-enabled' in params and params['fastk-enabled']:
conditions.append(dataframe['fastk'] < params['fastk-value'])
# TRIGGERS
# if 'trigger' in params:
# if params['trigger'] == 'bb_lower':
# conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
# if params['trigger'] == 'macd_cross_signal':
# conditions.append(qtpylib.crossed_above(
# dataframe['macd'], dataframe['macdsignal']
# ))
# if params['trigger'] == 'sar_reversal':
# conditions.append(qtpylib.crossed_above(
# dataframe['close'], dataframe['sar']
# ))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(15, 45, name='fastk-value'),
Integer(20, 50, name='adx-value'),
# Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='fastk-enabled'),
# Categorical([True, False], name='rsi-enabled'),
# Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by Hyperopt.
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value'])
if 'sell-fastk-enabled' in params and params['sell-fastk-enabled']:
conditions.append(dataframe['fastk'] > params['sell-fastk-value'])
if 'sell-cci-enabled' in params and params['sell-cci-enabled']:
conditions.append(dataframe['cci'] > params['sell-cci-value'])
# TRIGGERS
# if 'sell-trigger' in params:
# if params['sell-trigger'] == 'sell-bb_upper':
# conditions.append(dataframe['close'] > dataframe['bb_upperband'])
# if params['sell-trigger'] == 'sell-macd_cross_signal':
# conditions.append(qtpylib.crossed_above(
# dataframe['macdsignal'], dataframe['macd']
# ))
# if params['sell-trigger'] == 'sell-sar_reversal':
# conditions.append(qtpylib.crossed_above(
# dataframe['sar'], dataframe['close']
# ))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-fastk-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(100, 200, name='sell-cci-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-cci-enabled'),
Categorical([True, False], name='sell-fastk-enabled'),
# Categorical(['sell-bb_upper',
# 'sell-macd_cross_signal',
# 'sell-sar_reversal'], name='sell-trigger')
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(dataframe['open'] < dataframe['ema_low']) &
(dataframe['adx'] > 30) &
(dataframe['mfi'] < 30) &
(
(dataframe['fastk'] < 30) &
(dataframe['fastd'] < 30) &
(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']))
) &
(dataframe['resample_sma'] < dataframe['close'])
)
# |
# # try to get some sure things independent of resample
# ((dataframe['rsi'] - dataframe['mfi']) < 10) &
# (dataframe['mfi'] < 30) &
# (dataframe['cci'] < -200)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(
(dataframe['open'] >= dataframe['ema_high'])
) |
(
(qtpylib.crossed_above(dataframe['fastk'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
)
) & (dataframe['cci'] > 100)
)
,
'sell'] = 1
return dataframe

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# --- 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
class InformativeSample(IStrategy):
"""
Sample strategy implementing Informative Pairs - compares stake_currency with USDT.
Not performing very well - but should serve as an example how to use a referential pair against USDT.
author@: xmatthias
github@: https://github.com/freqtrade/freqtrade-strategies
How to use it?
> python3 freqtrade -s InformativeSample
"""
# 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 = '5m'
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
# 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 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)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [(f"{self.config['stake_currency']}/USDT", self.timeframe)]
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.
"""
dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
if self.dp:
# Get ohlcv data for informative pair.
data = self.dp.get_pair_dataframe(pair=f"{self.stake_currency}/USDT",
timeframe=self.timeframe)
# Combine the 2 dataframes using 'close'.
# This will result in a column named 'closeETH' or 'closeBTC' - depending on stake_currency.
dataframe = dataframe.merge(data[["date", "close"]], on="date", how="left", suffixes=("", self.config['stake_currency']))
# Calculate SMA20 on 'close' data for stake_currency/USDT. Resulting column is named as 'smaETH20' (if stake_currency is ETH)
dataframe[f"sma{self.config['stake_currency']}20"] = dataframe[f'close{self.stake_currency}'].rolling(20).mean()
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['ema20'] > dataframe['ema50']) &
# stake/USDT above sma(stake/USDT, 20)
(dataframe[f'close{self.stake_currency}'] > dataframe[f'sma{self.stake_currency}20'])
),
'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['ema20'] < dataframe['ema50']) &
# stake/USDT below sma(stake/USDT, 20)
(dataframe[f'close{self.stake_currency}'] < dataframe[f'sma{self.stake_currency}20'])
),
'sell'] = 1
return dataframe

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freqtrade-strategies-master/user_data/strategies/Strategy001.py Normal file
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# --- 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
class Strategy001(IStrategy):
"""
Strategy 001
author@: Gerald Lonlas
github@: https://github.com/freqtrade/freqtrade-strategies
How to use it?
> python3 ./freqtrade/main.py -s Strategy001
"""
# 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 = '5m'
# 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)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
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.
"""
dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
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[
(
qtpylib.crossed_above(dataframe['ema20'], dataframe['ema50']) &
(dataframe['ha_close'] > dataframe['ema20']) &
(dataframe['ha_open'] < dataframe['ha_close']) # green bar
),
'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[
(
qtpylib.crossed_above(dataframe['ema50'], dataframe['ema100']) &
(dataframe['ha_close'] < dataframe['ema20']) &
(dataframe['ha_open'] > dataframe['ha_close']) # red bar
),
'sell'] = 1
return dataframe

135
freqtrade-strategies-master/user_data/strategies/Strategy002.py Normal file
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# --- 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 = '5m'
# 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)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
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'] > 0.3)
),
'sell'] = 1
return dataframe

152
freqtrade-strategies-master/user_data/strategies/Strategy003.py Normal file
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# --- 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 Strategy003(IStrategy):
"""
Strategy 003
author@: Gerald Lonlas
github@: https://github.com/freqtrade/freqtrade-strategies
How to use it?
> python3 ./freqtrade/main.py -s Strategy003
"""
# 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 = '5m'
# 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)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
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.
"""
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# 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']
# EMA - Exponential Moving Average
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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'] < 28) &
(dataframe['rsi'] > 0) &
(dataframe['close'] < dataframe['sma']) &
(dataframe['fisher_rsi'] < -0.94) &
(dataframe['mfi'] < 16.0) &
(
(dataframe['ema50'] > dataframe['ema100']) |
(qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10']))
) &
(dataframe['fastd'] > dataframe['fastk']) &
(dataframe['fastd'] > 0)
),
'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'] > 0.3)
),
'sell'] = 1
return dataframe

154
freqtrade-strategies-master/user_data/strategies/Strategy004.py Normal file
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# --- 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
class Strategy004(IStrategy):
"""
Strategy 004
author@: Gerald Lonlas
github@: https://github.com/freqtrade/freqtrade-strategies
How to use it?
> python3 ./freqtrade/main.py -s Strategy004
"""
# 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 = '5m'
# 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)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
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.
"""
# ADX
dataframe['adx'] = ta.ADX(dataframe)
dataframe['slowadx'] = ta.ADX(dataframe, 35)
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# Stoch
stoch = ta.STOCHF(dataframe, 5)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['fastk-previous'] = dataframe.fastk.shift(1)
dataframe['fastd-previous'] = dataframe.fastd.shift(1)
# Slow Stoch
slowstoch = ta.STOCHF(dataframe, 50)
dataframe['slowfastd'] = slowstoch['fastd']
dataframe['slowfastk'] = slowstoch['fastk']
dataframe['slowfastk-previous'] = dataframe.slowfastk.shift(1)
dataframe['slowfastd-previous'] = dataframe.slowfastd.shift(1)
# EMA - Exponential Moving Average
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['mean-volume'] = dataframe['volume'].mean()
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['adx'] > 50) |
(dataframe['slowadx'] > 26)
) &
(dataframe['cci'] < -100) &
(
(dataframe['fastk-previous'] < 20) &
(dataframe['fastd-previous'] < 20)
) &
(
(dataframe['slowfastk-previous'] < 30) &
(dataframe['slowfastd-previous'] < 30)
) &
(dataframe['fastk-previous'] < dataframe['fastd-previous']) &
(dataframe['fastk'] > dataframe['fastd']) &
(dataframe['mean-volume'] > 0.75) &
(dataframe['close'] > 0.00000100)
),
'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['slowadx'] < 25) &
((dataframe['fastk'] > 70) | (dataframe['fastd'] > 70)) &
(dataframe['fastk-previous'] < dataframe['fastd-previous']) &
(dataframe['close'] > dataframe['ema5'])
),
'sell'] = 1
return dataframe

158
freqtrade-strategies-master/user_data/strategies/Strategy005.py Normal file
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# --- 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 Strategy005(IStrategy):
"""
Strategy 005
author@: Gerald Lonlas
github@: https://github.com/freqtrade/freqtrade-strategies
How to use it?
> python3 ./freqtrade/main.py -s Strategy005
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"1440": 0.01,
"80": 0.02,
"40": 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 = '5m'
# 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)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
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.
"""
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
# Minus Directional Indicator / Movement
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# 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)
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Overlap Studies
# ------------------------------------
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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[
# Prod
(
(dataframe['close'] > 0.00000200) &
(dataframe['volume'] > dataframe['volume'].rolling(200).mean() * 4) &
(dataframe['close'] < dataframe['sma']) &
(dataframe['fastd'] > dataframe['fastk']) &
(dataframe['rsi'] > 0) &
(dataframe['fastd'] > 0) &
# (dataframe['fisher_rsi'] < -0.94)
(dataframe['fisher_rsi_norma'] < 38.900000000000006)
),
'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[
# Prod
(
(qtpylib.crossed_above(dataframe['rsi'], 50)) &
(dataframe['macd'] < 0) &
(dataframe['minus_di'] > 0)
) |
(
(dataframe['sar'] > dataframe['close']) &
(dataframe['fisher_rsi'] > 0.3)
),
'sell'] = 1
return dataframe

68
freqtrade-strategies-master/user_data/strategies/berlinguyinca/ADXMomentum.py Normal file
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# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
# --------------------------------
class ADXMomentum(IStrategy):
"""
author@: Gert Wohlgemuth
converted from:
https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/AdxMomentum.cs
"""
# Minimal ROI designed for the strategy.
# adjust based on market conditions. We would recommend to keep it low for quick turn arounds
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.01
}
# Optimal stoploss designed for the strategy
stoploss = -0.25
# Optimal timeframe for the strategy
timeframe = '1h'
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 20
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=25)
dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=25)
dataframe['sar'] = ta.SAR(dataframe)
dataframe['mom'] = ta.MOM(dataframe, timeperiod=14)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 25) &
(dataframe['mom'] > 0) &
(dataframe['minus_di'] > 25) &
(dataframe['plus_di'] > dataframe['minus_di'])
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 25) &
(dataframe['mom'] < 0) &
(dataframe['minus_di'] > 25) &
(dataframe['plus_di'] < dataframe['minus_di'])
),
'sell'] = 1
return dataframe

85
freqtrade-strategies-master/user_data/strategies/berlinguyinca/ASDTSRockwellTrading.py Normal file
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# --- 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
class ASDTSRockwellTrading(IStrategy):
"""
trading strategy based on the concept explained at https://www.youtube.com/watch?v=mmAWVmKN4J0
author@: Gert Wohlgemuth
idea:
uptrend definition:
MACD above 0 line AND above MACD signal
downtrend definition:
MACD below 0 line and below MACD signal
sell definition:
MACD below MACD signal
it's basically a very simple MACD based strategy and we ignore the definition of the entry and exit points in this case, since the trading bot, will take of this already
"""
# 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 = -0.3
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
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['macd'] > 0) &
(dataframe['macd'] > dataframe['macdsignal'])
),
'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['macd'] < dataframe['macdsignal'])
),
'sell'] = 1
return dataframe

60
freqtrade-strategies-master/user_data/strategies/berlinguyinca/AdxSmas.py Normal file
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# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# --------------------------------
class AdxSmas(IStrategy):
"""
author@: Gert Wohlgemuth
converted from:
https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/AdxSmas.cs
"""
# Minimal ROI designed for the strategy.
# adjust based on market conditions. We would recommend to keep it low for quick turn arounds
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.1
}
# Optimal stoploss designed for the strategy
stoploss = -0.25
# Optimal timeframe for the strategy
timeframe = '1h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
dataframe['short'] = ta.SMA(dataframe, timeperiod=3)
dataframe['long'] = ta.SMA(dataframe, timeperiod=6)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 25) &
(qtpylib.crossed_above(dataframe['short'], dataframe['long']))
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] < 25) &
(qtpylib.crossed_above(dataframe['long'], dataframe['short']))
),
'sell'] = 1
return dataframe

64
freqtrade-strategies-master/user_data/strategies/berlinguyinca/AverageStrategy.py Normal file
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# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class AverageStrategy(IStrategy):
"""
author@: Gert Wohlgemuth
idea:
buys and sells on crossovers - doesn't really perfom that well and its just a proof of concept
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.5
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.2
# Optimal timeframe for the strategy
timeframe = '4h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['maShort'] = ta.EMA(dataframe, timeperiod=8)
dataframe['maMedium'] = ta.EMA(dataframe, timeperiod=21)
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[
(
qtpylib.crossed_above(dataframe['maShort'], dataframe['maMedium'])
),
'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[
(
qtpylib.crossed_above(dataframe['maMedium'], dataframe['maShort'])
),
'sell'] = 1
return dataframe

66
freqtrade-strategies-master/user_data/strategies/berlinguyinca/AwesomeMacd.py Normal file
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# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# --------------------------------
class AwesomeMacd(IStrategy):
"""
author@: Gert Wohlgemuth
converted from:
https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/AwesomeMacd.cs
"""
# Minimal ROI designed for the strategy.
# adjust based on market conditions. We would recommend to keep it low for quick turn arounds
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.1
}
# Optimal stoploss designed for the strategy
stoploss = -0.25
# Optimal timeframe for the strategy
timeframe = '1h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['macd'] > 0) &
(dataframe['ao'] > 0) &
(dataframe['ao'].shift() < 0)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['macd'] < 0) &
(dataframe['ao'] < 0) &
(dataframe['ao'].shift() > 0)
),
'sell'] = 1
return dataframe

63
freqtrade-strategies-master/user_data/strategies/berlinguyinca/BbandRsi.py Normal file
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# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# --------------------------------
class BbandRsi(IStrategy):
"""
author@: Gert Wohlgemuth
converted from:
https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/BbandRsi.cs
"""
# Minimal ROI designed for the strategy.
# adjust based on market conditions. We would recommend to keep it low for quick turn arounds
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.1
}
# Optimal stoploss designed for the strategy
stoploss = -0.25
# Optimal timeframe for the strategy
timeframe = '1h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] < 30) &
(dataframe['close'] < dataframe['bb_lowerband'])
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] > 70)
),
'sell'] = 1
return dataframe

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freqtrade-strategies-master/user_data/strategies/berlinguyinca/BinHV27.py Normal file
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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
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class BinHV27(IStrategy):
"""
strategy sponsored by user BinH from slack
"""
minimal_roi = {
"0": 1
}
stoploss = -0.50
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5))
rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'})
dataframe['emarsi'] = numpy.nan_to_num(ta.EMA(rsiframe, timeperiod=5))
dataframe['adx'] = numpy.nan_to_num(ta.ADX(dataframe))
dataframe['minusdi'] = numpy.nan_to_num(ta.MINUS_DI(dataframe))
minusdiframe = DataFrame(dataframe['minusdi']).rename(columns={'minusdi': 'close'})
dataframe['minusdiema'] = numpy.nan_to_num(ta.EMA(minusdiframe, timeperiod=25))
dataframe['plusdi'] = numpy.nan_to_num(ta.PLUS_DI(dataframe))
plusdiframe = DataFrame(dataframe['plusdi']).rename(columns={'plusdi': 'close'})
dataframe['plusdiema'] = numpy.nan_to_num(ta.EMA(plusdiframe, timeperiod=5))
dataframe['lowsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=60))
dataframe['highsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=120))
dataframe['fastsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=120))
dataframe['slowsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=240))
dataframe['bigup'] = dataframe['fastsma'].gt(dataframe['slowsma']) & ((dataframe['fastsma'] - dataframe['slowsma']) > dataframe['close'] / 300)
dataframe['bigdown'] = ~dataframe['bigup']
dataframe['trend'] = dataframe['fastsma'] - dataframe['slowsma']
dataframe['preparechangetrend'] = dataframe['trend'].gt(dataframe['trend'].shift())
dataframe['preparechangetrendconfirm'] = dataframe['preparechangetrend'] & dataframe['trend'].shift().gt(dataframe['trend'].shift(2))
dataframe['continueup'] = dataframe['slowsma'].gt(dataframe['slowsma'].shift()) & dataframe['slowsma'].shift().gt(dataframe['slowsma'].shift(2))
dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift()
dataframe['slowingdown'] = dataframe['delta'].lt(dataframe['delta'].shift())
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
dataframe['slowsma'].gt(0) &
dataframe['close'].lt(dataframe['highsma']) &
dataframe['close'].lt(dataframe['lowsma']) &
dataframe['minusdi'].gt(dataframe['minusdiema']) &
dataframe['rsi'].ge(dataframe['rsi'].shift()) &
(
(
~dataframe['preparechangetrend'] &
~dataframe['continueup'] &
dataframe['adx'].gt(25) &
dataframe['bigdown'] &
dataframe['emarsi'].le(20)
) |
(
~dataframe['preparechangetrend'] &
dataframe['continueup'] &
dataframe['adx'].gt(30) &
dataframe['bigdown'] &
dataframe['emarsi'].le(20)
) |
(
~dataframe['continueup'] &
dataframe['adx'].gt(35) &
dataframe['bigup'] &
dataframe['emarsi'].le(20)
) |
(
dataframe['continueup'] &
dataframe['adx'].gt(30) &
dataframe['bigup'] &
dataframe['emarsi'].le(25)
)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
~dataframe['preparechangetrendconfirm'] &
~dataframe['continueup'] &
(dataframe['close'].gt(dataframe['lowsma']) | dataframe['close'].gt(dataframe['highsma'])) &
dataframe['highsma'].gt(0) &
dataframe['bigdown']
) |
(
~dataframe['preparechangetrendconfirm'] &
~dataframe['continueup'] &
dataframe['close'].gt(dataframe['highsma']) &
dataframe['highsma'].gt(0) &
(dataframe['emarsi'].ge(75) | dataframe['close'].gt(dataframe['slowsma'])) &
dataframe['bigdown']
) |
(
~dataframe['preparechangetrendconfirm'] &
dataframe['close'].gt(dataframe['highsma']) &
dataframe['highsma'].gt(0) &
dataframe['adx'].gt(30) &
dataframe['emarsi'].ge(80) &
dataframe['bigup']
) |
(
dataframe['preparechangetrendconfirm'] &
~dataframe['continueup'] &
dataframe['slowingdown'] &
dataframe['emarsi'].ge(75) &
dataframe['slowsma'].gt(0)
) |
(
dataframe['preparechangetrendconfirm'] &
dataframe['minusdi'].lt(dataframe['plusdi']) &
dataframe['close'].gt(dataframe['lowsma']) &
dataframe['slowsma'].gt(0)
)
),
'sell'] = 1
return dataframe

57
freqtrade-strategies-master/user_data/strategies/berlinguyinca/BinHV45.py Normal file
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# --- 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 numpy as np
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
def bollinger_bands(stock_price, window_size, num_of_std):
rolling_mean = stock_price.rolling(window=window_size).mean()
rolling_std = stock_price.rolling(window=window_size).std()
lower_band = rolling_mean - (rolling_std * num_of_std)
return rolling_mean, lower_band
class BinHV45(IStrategy):
minimal_roi = {
"0": 0.0125
}
stoploss = -0.05
timeframe = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
dataframe['mid'] = np.nan_to_num(mid)
dataframe['lower'] = np.nan_to_num(lower)
dataframe['bbdelta'] = (dataframe['mid'] - dataframe['lower']).abs()
dataframe['pricedelta'] = (dataframe['open'] - dataframe['close']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
dataframe['lower'].shift().gt(0) &
dataframe['bbdelta'].gt(dataframe['close'] * 0.008) &
dataframe['closedelta'].gt(dataframe['close'] * 0.0175) &
dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) &
dataframe['close'].lt(dataframe['lower'].shift()) &
dataframe['close'].le(dataframe['close'].shift())
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
no sell signal
"""
dataframe['sell'] = 0
return dataframe

119
freqtrade-strategies-master/user_data/strategies/berlinguyinca/CCIStrategy.py Normal file
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# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, Series, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class CCIStrategy(IStrategy):
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.1
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.02
# Optimal timeframe for the strategy
timeframe = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = self.resample(dataframe, self.timeframe, 5)
dataframe['cci_one'] = ta.CCI(dataframe, timeperiod=170)
dataframe['cci_two'] = ta.CCI(dataframe, timeperiod=34)
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['cmf'] = self.chaikin_mf(dataframe)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
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['cci_one'] < -100)
& (dataframe['cci_two'] < -100)
& (dataframe['cmf'] < -0.1)
& (dataframe['mfi'] < 25)
# insurance
& (dataframe['resample_medium'] > dataframe['resample_short'])
& (dataframe['resample_long'] < dataframe['close'])
),
'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['cci_one'] > 100)
& (dataframe['cci_two'] > 100)
& (dataframe['cmf'] > 0.3)
& (dataframe['resample_sma'] < dataframe['resample_medium'])
& (dataframe['resample_medium'] < dataframe['resample_short'])
),
'sell'] = 1
return dataframe
def chaikin_mf(self, df, periods=20):
close = df['close']
low = df['low']
high = df['high']
volume = df['volume']
mfv = ((close - low) - (high - close)) / (high - low)
mfv = mfv.fillna(0.0) # float division by zero
mfv *= volume
cmf = mfv.rolling(periods).sum() / volume.rolling(periods).sum()
return Series(cmf, name='cmf')
def resample(self, dataframe, interval, factor):
# defines the reinforcement logic
# resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
df = dataframe.copy()
df = df.set_index(DatetimeIndex(df['date']))
ohlc_dict = {
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last'
}
df = df.resample(str(int(interval[:-1]) * factor) + 'min', label="right").agg(ohlc_dict)
df['resample_sma'] = ta.SMA(df, timeperiod=100, price='close')
df['resample_medium'] = ta.SMA(df, timeperiod=50, price='close')
df['resample_short'] = ta.SMA(df, timeperiod=25, price='close')
df['resample_long'] = ta.SMA(df, timeperiod=200, price='close')
df = df.drop(columns=['open', 'high', 'low', 'close'])
df = df.resample(interval[:-1] + 'min')
df = df.interpolate(method='time')
df['date'] = df.index
df.index = range(len(df))
dataframe = merge(dataframe, df, on='date', how='left')
return dataframe

95
freqtrade-strategies-master/user_data/strategies/berlinguyinca/CMCWinner.py Normal file
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# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
# This class is a sample. Feel free to customize it.
class CMCWinner(IStrategy):
"""
This is a test strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"40": 0.0,
"30": 0.02,
"20": 0.03,
"0": 0.05
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.05
# Optimal timeframe for the strategy
timeframe = '15m'
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.
"""
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# CMO
dataframe['cmo'] = ta.CMO(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['cci'].shift(1) < -100) &
(dataframe['mfi'].shift(1) < 20) &
(dataframe['cmo'].shift(1) < -50)
),
'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['cci'].shift(1) > 100) &
(dataframe['mfi'].shift(1) > 80) &
(dataframe['cmo'].shift(1) > 50)
),
'sell'] = 1
return dataframe

83
freqtrade-strategies-master/user_data/strategies/berlinguyinca/ClucMay72018.py Normal file
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# --- 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
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class ClucMay72018(IStrategy):
"""
author@: Gert Wohlgemuth
works on new objectify branch!
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.01
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.05
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=5)
rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'})
dataframe['emarsi'] = ta.EMA(rsiframe, timeperiod=5)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['adx'] = ta.ADX(dataframe)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=50)
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['close'] < dataframe['ema100']) &
(dataframe['close'] < 0.985 * dataframe['bb_lowerband']) &
(dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20))
),
'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['close'] > dataframe['bb_middleband'])
),
'sell'] = 1
return dataframe

80
freqtrade-strategies-master/user_data/strategies/berlinguyinca/CofiBitStrategy.py Normal file
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# --- Do not remove these libs ---
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
class CofiBitStrategy(IStrategy):
"""
taken from slack by user CofiBit
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"40": 0.05,
"30": 0.06,
"20": 0.07,
"0": 0.10
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.25
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low')
dataframe['adx'] = ta.ADX(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['open'] < dataframe['ema_low']) &
(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) &
# (dataframe['fastk'] > dataframe['fastd']) &
(dataframe['fastk'] < 30) &
(dataframe['fastd'] < 30) &
(dataframe['adx'] > 30)
),
'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['open'] >= dataframe['ema_high'])
) |
(
# (dataframe['fastk'] > 70) &
# (dataframe['fastd'] > 70)
(qtpylib.crossed_above(dataframe['fastk'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
),
'sell'] = 1
return dataframe

75
freqtrade-strategies-master/user_data/strategies/berlinguyinca/CombinedBinHAndCluc.py Normal file
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# --- Do not remove these libs ---
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
# --------------------------------
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
def bollinger_bands(stock_price, window_size, num_of_std):
rolling_mean = stock_price.rolling(window=window_size).mean()
rolling_std = stock_price.rolling(window=window_size).std()
lower_band = rolling_mean - (rolling_std * num_of_std)
return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)
class CombinedBinHAndCluc(IStrategy):
# Based on a backtesting:
# - the best perfomance is reached with "max_open_trades" = 2 (in average for any market),
# so it is better to increase "stake_amount" value rather then "max_open_trades" to get more profit
# - if the market is constantly green(like in JAN 2018) the best performance is reached with
# "max_open_trades" = 2 and minimal_roi = 0.01
minimal_roi = {
"0": 0.05
}
stoploss = -0.05
timeframe = '5m'
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# strategy BinHV45
mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
dataframe['lower'] = lower
dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
# strategy ClucMay72018
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
( # strategy BinHV45
dataframe['lower'].shift().gt(0) &
dataframe['bbdelta'].gt(dataframe['close'] * 0.008) &
dataframe['closedelta'].gt(dataframe['close'] * 0.0175) &
dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) &
dataframe['close'].lt(dataframe['lower'].shift()) &
dataframe['close'].le(dataframe['close'].shift())
) |
( # strategy ClucMay72018
(dataframe['close'] < dataframe['ema_slow']) &
(dataframe['close'] < 0.985 * dataframe['bb_lowerband']) &
(dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 20))
),
'buy'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
"""
dataframe.loc[
(dataframe['close'] > dataframe['bb_middleband']),
'sell'
] = 1
return dataframe

44
freqtrade-strategies-master/user_data/strategies/berlinguyinca/DoesNothingStrategy.py Normal file
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# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
class DoesNothingStrategy(IStrategy):
"""
author@: Gert Wohlgemuth
just a skeleton
"""
# Minimal ROI designed for the strategy.
# adjust based on market conditions. We would recommend to keep it low for quick turn arounds
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.01
}
# Optimal stoploss designed for the strategy
stoploss = -0.25
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
),
'sell'] = 1
return dataframe

85
freqtrade-strategies-master/user_data/strategies/berlinguyinca/EMASkipPump.py Normal file
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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 EMASkipPump(IStrategy):
"""
basic strategy, which trys to avoid pump and dump market conditions. Shared from the tradingview
slack
"""
EMA_SHORT_TERM = 5
EMA_MEDIUM_TERM = 12
EMA_LONG_TERM = 21
# Minimal ROI designed for the strategy.
# we only sell after 100%, unless our sell points are found before
minimal_roi = {
"0": 0.1
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
# should be converted to a trailing stop loss
stoploss = -0.05
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
""" Adds several different TA indicators to the given DataFrame
"""
dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_SHORT_TERM
)
dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_MEDIUM_TERM
)
dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_LONG_TERM
)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=20, stds=2
)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20)) &
(dataframe['close'] < dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
(dataframe['close'] < dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
(dataframe['close'] == dataframe['min']) &
(dataframe['close'] <= dataframe['bb_lowerband']),
'buy'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(dataframe['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
(dataframe['close'] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
(dataframe['close'] >= dataframe['max']) &
(dataframe['close'] >= dataframe['bb_upperband']),
'sell'
] = 1
return dataframe

49
freqtrade-strategies-master/user_data/strategies/berlinguyinca/Freqtrade_backtest_validation_freqtrade1.py Normal file
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# Freqtrade_backtest_validation_freqtrade1.py
# This script is 1 of a pair the other being freqtrade_backtest_validation_tradingview1
# These should be executed on their respective platforms for the same coin/period/resolution
# The purpose is to test Freqtrade backtest provides like results to a known industry platform.
#
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class Freqtrade_backtest_validation_freqtrade1(IStrategy):
# Minimal ROI designed for the strategy.
minimal_roi = {
"40": 2.0,
"30": 2.01,
"20": 2.02,
"0": 2.04
}
stoploss = -0.90
timeframe = '1h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# SMA - Simple Moving Average
dataframe['fastMA'] = ta.SMA(dataframe, timeperiod=14)
dataframe['slowMA'] = ta.SMA(dataframe, timeperiod=28)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['fastMA'] > dataframe['slowMA'])
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['fastMA'] < dataframe['slowMA'])
),
'sell'] = 1
return dataframe

108
freqtrade-strategies-master/user_data/strategies/berlinguyinca/Low_BB.py Normal file
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# --- 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
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# import numpy as np # noqa
class Low_BB(IStrategy):
"""
author@: Thorsten
works on new objectify branch!
idea:
buy after crossing .98 * lower_bb and sell if trailing stop loss is hit
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.9,
"1": 0.05,
"10": 0.04,
"15": 0.5
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.015
# Optimal timeframe for the strategy
timeframe = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
##################################################################################
# buy and sell indicators
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=20, stds=2
)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# dataframe['cci'] = ta.CCI(dataframe)
# dataframe['mfi'] = ta.MFI(dataframe)
# dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
# dataframe['canbuy'] = np.NaN
# dataframe['canbuy2'] = np.NaN
# dataframe.loc[dataframe.close.rolling(49).min() <= 1.1 * dataframe.close, 'canbuy'] == 1
# dataframe.loc[dataframe.close.rolling(600).max() < 1.2 * dataframe.close, 'canbuy'] = 1
# dataframe.loc[dataframe.close.rolling(600).max() * 0.8 > dataframe.close, 'canbuy2'] = 1
##################################################################################
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
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['close'] <= 0.98 * dataframe['bb_lowerband'])
)
,
'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[
(),
'sell'] = 1
return dataframe

83
freqtrade-strategies-master/user_data/strategies/berlinguyinca/MACDStrategy.py Normal file
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# --- 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
class MACDStrategy(IStrategy):
"""
author@: Gert Wohlgemuth
idea:
uptrend definition:
MACD above MACD signal
and CCI < -50
downtrend definition:
MACD below MACD signal
and CCI > 100
"""
# 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 = -0.3
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['cci'] = ta.CCI(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['macd'] > dataframe['macdsignal']) &
(dataframe['cci'] <= -50.0)
),
'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['macd'] < dataframe['macdsignal']) &
(dataframe['cci'] >= 100.0)
),
'sell'] = 1
return dataframe

77
freqtrade-strategies-master/user_data/strategies/berlinguyinca/MACDStrategy_crossed.py Normal file
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# --- 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
class MACDStrategy_crossed(IStrategy):
"""
buy:
MACD crosses MACD signal above
and CCI < -50
sell:
MACD crosses MACD signal below
and CCI > 100
"""
# 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 = -0.3
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['cci'] = ta.CCI(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[
(
qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']) &
(dataframe['cci'] <= -50.0)
),
'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[
(
qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal']) &
(dataframe['cci'] >= 100.0)
),
'sell'] = 1
return dataframe

70
freqtrade-strategies-master/user_data/strategies/berlinguyinca/MultiRSI.py Normal file
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# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
from technical.util import resample_to_interval, resampled_merge
class MultiRSI(IStrategy):
"""
author@: Gert Wohlgemuth
based on work from Creslin
"""
minimal_roi = {
"0": 0.01
}
# Optimal stoploss designed for the strategy
stoploss = -0.05
# Optimal timeframe for the strategy
timeframe = '5m'
def get_ticker_indicator(self):
return int(self.timeframe[:-1])
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
# resample our dataframes
dataframe_short = resample_to_interval(dataframe, self.get_ticker_indicator() * 2)
dataframe_long = resample_to_interval(dataframe, self.get_ticker_indicator() * 8)
# compute our RSI's
dataframe_short['rsi'] = ta.RSI(dataframe_short, timeperiod=14)
dataframe_long['rsi'] = ta.RSI(dataframe_long, timeperiod=14)
# merge dataframe back together
dataframe = resampled_merge(dataframe, dataframe_short)
dataframe = resampled_merge(dataframe, dataframe_long)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe.fillna(method='ffill', inplace=True)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
# must be bearish
(dataframe['sma5'] >= dataframe['sma200']) &
(dataframe['rsi'] < (dataframe['resample_{}_rsi'.format(self.get_ticker_indicator() * 8)] - 20))
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] > dataframe['resample_{}_rsi'.format(self.get_ticker_indicator()*2)]) &
(dataframe['rsi'] > dataframe['resample_{}_rsi'.format(self.get_ticker_indicator()*8)])
),
'sell'] = 1
return dataframe

77
freqtrade-strategies-master/user_data/strategies/berlinguyinca/Quickie.py Normal file
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# --- 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
class Quickie(IStrategy):
"""
author@: Gert Wohlgemuth
idea:
momentum based strategie. The main idea is that it closes trades very quickly, while avoiding excessive losses. Hence a rather moderate stop loss in this case
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"100": 0.01,
"30": 0.03,
"15": 0.06,
"10": 0.15,
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.25
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=200)
dataframe['adx'] = ta.ADX(dataframe)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 30) &
(dataframe['tema'] < dataframe['bb_middleband']) &
(dataframe['tema'] > dataframe['tema'].shift(1)) &
(dataframe['sma_200'] > dataframe['close'])
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 70) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
),
'sell'] = 1
return dataframe

96
freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedAverageStrategy.py Normal file
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# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, merge, DatetimeIndex
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.util import resample_to_interval, resampled_merge
from freqtrade.exchange import timeframe_to_minutes
class ReinforcedAverageStrategy(IStrategy):
"""
author@: Gert Wohlgemuth
idea:
buys and sells on crossovers - doesn't really perfom that well and its just a proof of concept
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.5
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.2
# Optimal timeframe for the strategy
timeframe = '4h'
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
trailing_only_offset_is_reached = False
# 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 = False
ignore_roi_if_buy_signal = False
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['maShort'] = ta.EMA(dataframe, timeperiod=8)
dataframe['maMedium'] = ta.EMA(dataframe, timeperiod=21)
##################################################################################
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
self.resample_interval = timeframe_to_minutes(self.timeframe) * 12
dataframe_long = resample_to_interval(dataframe, self.resample_interval)
dataframe_long['sma'] = ta.SMA(dataframe_long, timeperiod=50, price='close')
dataframe = resampled_merge(dataframe, dataframe_long, fill_na=True)
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[
(
qtpylib.crossed_above(dataframe['maShort'], dataframe['maMedium']) &
(dataframe['close'] > dataframe[f'resample_{self.resample_interval}_sma']) &
(dataframe['volume'] > 0)
),
'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[
(
qtpylib.crossed_above(dataframe['maMedium'], dataframe['maShort']) &
(dataframe['volume'] > 0)
),
'sell'] = 1
return dataframe

194
freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedQuickie.py Normal file
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# --- 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
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class ReinforcedQuickie(IStrategy):
"""
author@: Gert Wohlgemuth
works on new objectify branch!
idea:
only buy on an upward tending market
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.01
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.05
# Optimal timeframe for the strategy
timeframe = '5m'
# resample factor to establish our general trend. Basically don't buy if a trend is not given
resample_factor = 12
EMA_SHORT_TERM = 5
EMA_MEDIUM_TERM = 12
EMA_LONG_TERM = 21
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = self.resample(dataframe, self.timeframe, self.resample_factor)
##################################################################################
# buy and sell indicators
dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_SHORT_TERM
)
dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_MEDIUM_TERM
)
dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_LONG_TERM
)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=20, stds=2
)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
dataframe['cci'] = ta.CCI(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4
##################################################################################
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
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['close'] < dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
(dataframe['close'] < dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
(dataframe['close'] == dataframe['min']) &
(dataframe['close'] <= dataframe['bb_lowerband'])
)
|
# simple v bottom shape (lopsided to the left to increase reactivity)
# which has to be below a very slow average
# this pattern only catches a few, but normally very good buy points
(
(dataframe['average'].shift(5) > dataframe['average'].shift(4))
& (dataframe['average'].shift(4) > dataframe['average'].shift(3))
& (dataframe['average'].shift(3) > dataframe['average'].shift(2))
& (dataframe['average'].shift(2) > dataframe['average'].shift(1))
& (dataframe['average'].shift(1) < dataframe['average'].shift(0))
& (dataframe['low'].shift(1) < dataframe['bb_middleband'])
& (dataframe['cci'].shift(1) < -100)
& (dataframe['rsi'].shift(1) < 30)
& (dataframe['mfi'].shift(1) < 30)
)
)
# safeguard against down trending markets and a pump and dump
&
(
(dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20)) &
(dataframe['resample_sma'] < dataframe['close']) &
(dataframe['resample_sma'].shift(1) < dataframe['resample_sma'])
)
)
,
'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['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
(dataframe['close'] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
(dataframe['close'] >= dataframe['max']) &
(dataframe['close'] >= dataframe['bb_upperband']) &
(dataframe['mfi'] > 80)
) |
# always sell on eight green candles
# with a high rsi
(
(dataframe['open'] < dataframe['close']) &
(dataframe['open'].shift(1) < dataframe['close'].shift(1)) &
(dataframe['open'].shift(2) < dataframe['close'].shift(2)) &
(dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
(dataframe['open'].shift(4) < dataframe['close'].shift(4)) &
(dataframe['open'].shift(5) < dataframe['close'].shift(5)) &
(dataframe['open'].shift(6) < dataframe['close'].shift(6)) &
(dataframe['open'].shift(7) < dataframe['close'].shift(7)) &
(dataframe['rsi'] > 70)
)
,
'sell'
] = 1
return dataframe
def resample(self, dataframe, interval, factor):
# defines the reinforcement logic
# resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
df = dataframe.copy()
df = df.set_index(DatetimeIndex(df['date']))
ohlc_dict = {
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last'
}
df = df.resample(str(int(interval[:-1]) * factor) + 'min',
label="right").agg(ohlc_dict).dropna(how='any')
df['resample_sma'] = ta.SMA(df, timeperiod=25, price='close')
df = df.drop(columns=['open', 'high', 'low', 'close'])
df = df.resample(interval[:-1] + 'min')
df = df.interpolate(method='time')
df['date'] = df.index
df.index = range(len(df))
dataframe = merge(dataframe, df, on='date', how='left')
return dataframe

102
freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedSmoothScalp.py Normal file
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# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import timeframe_to_minutes
from pandas import DataFrame
from technical.util import resample_to_interval, resampled_merge
import numpy # noqa
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class ReinforcedSmoothScalp(IStrategy):
"""
this strategy is based around the idea of generating a lot of potentatils buys and make tiny profits on each trade
we recommend to have at least 60 parallel trades at any time to cover non avoidable losses
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.02
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
# should not be below 3% loss
stoploss = -0.1
# Optimal timeframe for the strategy
# the shorter the better
timeframe = '1m'
# resample factor to establish our general trend. Basically don't buy if a trend is not given
resample_factor = 5
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
tf_res = timeframe_to_minutes(self.timeframe) * 5
df_res = resample_to_interval(dataframe, tf_res)
df_res['sma'] = ta.SMA(df_res, 50, price='close')
dataframe = resampled_merge(dataframe, df_res, fill_na=True)
dataframe['resample_sma'] = dataframe[f'resample_{tf_res}_sma']
dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low')
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['adx'] = ta.ADX(dataframe)
dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['mfi'] = ta.MFI(dataframe)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(dataframe['open'] < dataframe['ema_low']) &
(dataframe['adx'] > 30) &
(dataframe['mfi'] < 30) &
(
(dataframe['fastk'] < 30) &
(dataframe['fastd'] < 30) &
(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']))
) &
(dataframe['resample_sma'] < dataframe['close'])
)
# |
# # try to get some sure things independent of resample
# ((dataframe['rsi'] - dataframe['mfi']) < 10) &
# (dataframe['mfi'] < 30) &
# (dataframe['cci'] < -200)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(
(dataframe['open'] >= dataframe['ema_high'])
) |
(
(qtpylib.crossed_above(dataframe['fastk'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
)
) & (dataframe['cci'] > 100)
)
,
'sell'] = 1
return dataframe

78
freqtrade-strategies-master/user_data/strategies/berlinguyinca/Scalp.py Normal file
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# --- 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 talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class Scalp(IStrategy):
"""
this strategy is based around the idea of generating a lot of potentatils buys and make tiny profits on each trade
we recommend to have at least 60 parallel trades at any time to cover non avoidable losses.
Recommended is to only sell based on ROI for this strategy
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.01
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
# should not be below 3% loss
stoploss = -0.04
# Optimal timeframe for the strategy
# the shorter the better
timeframe = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low')
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['adx'] = ta.ADX(dataframe)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['open'] < dataframe['ema_low']) &
(dataframe['adx'] > 30) &
(
(dataframe['fastk'] < 30) &
(dataframe['fastd'] < 30) &
(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']))
)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['open'] >= dataframe['ema_high'])
) |
(
(qtpylib.crossed_above(dataframe['fastk'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
),
'sell'] = 1
return dataframe

75
freqtrade-strategies-master/user_data/strategies/berlinguyinca/Simple.py Normal file
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# --- 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
class Simple(IStrategy):
"""
author@: Gert Wohlgemuth
idea:
this strategy is based on the book, 'The Simple Strategy' and can be found in detail here:
https://www.amazon.com/Simple-Strategy-Powerful-Trading-Futures-ebook/dp/B00E66QPCG/ref=sr_1_1?ie=UTF8&qid=1525202675&sr=8-1&keywords=the+simple+strategy
"""
# Minimal ROI designed for the strategy.
# adjust based on market conditions. We would recommend to keep it low for quick turn arounds
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.01
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.25
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=12, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(dataframe['macd'] > 0) # over 0
& (dataframe['macd'] > dataframe['macdsignal']) # over signal
& (dataframe['bb_upperband'] > dataframe['bb_upperband'].shift(1)) # pointed up
& (dataframe['rsi'] > 70) # optional filter, need to investigate
)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# different strategy used for sell points, due to be able to duplicate it to 100%
dataframe.loc[
(
(dataframe['rsi'] > 80)
),
'sell'] = 1
return dataframe

303
freqtrade-strategies-master/user_data/strategies/berlinguyinca/SmoothOperator.py Normal file
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# --- 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
# DO NOT USE, just playing with smooting and graphs!
class SmoothOperator(IStrategy):
"""
author@: Gert Wohlgemuth
idea:
The concept is about combining several common indicators, with a heavily smoothing, while trying to detect
a none completed peak shape.
"""
# Minimal ROI designed for the strategy.
# we only sell after 100%, unless our sell points are found before
minimal_roi = {
"0": 0.10
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
# should be converted to a trailing stop loss
stoploss = -0.05
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
##################################################################################
# required for entry and exit
# CCI
dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['adx'] = ta.ADX(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['mfi_smooth'] = ta.EMA(dataframe, timeperiod=11, price='mfi')
dataframe['cci_smooth'] = ta.EMA(dataframe, timeperiod=11, price='cci')
dataframe['rsi_smooth'] = ta.EMA(dataframe, timeperiod=11, price='rsi')
##################################################################################
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
##################################################################################
# required for entry
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=1.6)
dataframe['entry_bb_lowerband'] = bollinger['lower']
dataframe['entry_bb_upperband'] = bollinger['upper']
dataframe['entry_bb_middleband'] = bollinger['mid']
dataframe['bpercent'] = (dataframe['close'] - dataframe['bb_lowerband']) / (
dataframe['bb_upperband'] - dataframe['bb_lowerband']) * 100
dataframe['bsharp'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / (
dataframe['bb_middleband'])
# these seem to be kind useful to measure when bands widen
# but than they are directly based on the moving average
dataframe['bsharp_slow'] = ta.SMA(dataframe, price='bsharp', timeperiod=11)
dataframe['bsharp_medium'] = ta.SMA(dataframe, price='bsharp', timeperiod=8)
dataframe['bsharp_fast'] = ta.SMA(dataframe, price='bsharp', timeperiod=5)
##################################################################################
# rsi and mfi are slightly weighted
dataframe['mfi_rsi_cci_smooth'] = (dataframe['rsi_smooth'] * 1.125 + dataframe['mfi_smooth'] * 1.125 +
dataframe[
'cci_smooth']) / 3
dataframe['mfi_rsi_cci_smooth'] = ta.TEMA(dataframe, timeperiod=21, price='mfi_rsi_cci_smooth')
# playgound
dataframe['candle_size'] = (dataframe['close'] - dataframe['open']) * (
dataframe['close'] - dataframe['open']) / 2
# helps with pattern recognition
dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4
dataframe['sma_slow'] = ta.SMA(dataframe, timeperiod=200, price='close')
dataframe['sma_medium'] = ta.SMA(dataframe, timeperiod=100, price='close')
dataframe['sma_fast'] = ta.SMA(dataframe, timeperiod=50, price='close')
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
# protection against pump and dump
# (dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20))
#
# & (dataframe['macd'] < dataframe['macdsignal'])
# & (dataframe['macd'] > 0)
# # spike below entry band for 3 consecutive ticks
# & (dataframe['low'] < dataframe['entry_bb_lowerband'])
# & (dataframe['low'].shift(1) < dataframe['bb_lowerband'].shift(1))
# & (dataframe['low'].shift(2) < dataframe['bb_lowerband'].shift(2))
# # pattern recognition
# & (
# (dataframe['close'] > dataframe['open'])
# | (dataframe['CDLHAMMER'] == 100)
# | (dataframe['CDLINVERTEDHAMMER'] == 100)
# | (dataframe['CDLDRAGONFLYDOJI'] == 100)
# )
# bottom curve detection
# & (dataframe['mfi_rsi_cci_smooth'] < 0)
#
# |
(
# simple v bottom shape (lopsided to the left to increase reactivity)
# which has to be below a very slow average
# this pattern only catches a few, but normally very good buy points
(
(dataframe['average'].shift(5) > dataframe['average'].shift(4))
& (dataframe['average'].shift(4) > dataframe['average'].shift(3))
& (dataframe['average'].shift(3) > dataframe['average'].shift(2))
& (dataframe['average'].shift(2) > dataframe['average'].shift(1))
& (dataframe['average'].shift(1) < dataframe['average'].shift(0))
& (dataframe['low'].shift(1) < dataframe['bb_middleband'])
& (dataframe['cci'].shift(1) < -100)
& (dataframe['rsi'].shift(1) < 30)
)
|
# buy in very oversold conditions
(
(dataframe['low'] < dataframe['bb_middleband'])
& (dataframe['cci'] < -200)
& (dataframe['rsi'] < 30)
& (dataframe['mfi'] < 30)
)
|
# etc tends to trade like this
# over very long periods of slowly building up coins
# does not happen often, but once in a while
(
(dataframe['mfi'] < 10)
& (dataframe['cci'] < -150)
& (dataframe['rsi'] < dataframe['mfi'])
)
)
&
# ensure we have an overall uptrend
(dataframe['close'] > dataframe['close'].shift())
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# different strategy used for sell points, due to be able to duplicate it to 100%
dataframe.loc[
(
(
# This generates very nice sale points, and mostly sit's one stop behind
# the top of the peak
(
(dataframe['mfi_rsi_cci_smooth'] > 100)
& (dataframe['mfi_rsi_cci_smooth'].shift(1) > dataframe['mfi_rsi_cci_smooth'])
& (dataframe['mfi_rsi_cci_smooth'].shift(2) < dataframe['mfi_rsi_cci_smooth'].shift(1))
& (dataframe['mfi_rsi_cci_smooth'].shift(3) < dataframe['mfi_rsi_cci_smooth'].shift(2))
)
|
# This helps with very long, sideways trends, to get out of a market before
# it dumps
(
StrategyHelper.eight_green_candles(dataframe)
)
|
# in case of very overbought market, like some one pumping
# sell
(
(dataframe['cci'] > 200)
& (dataframe['rsi'] > 70)
)
)
),
'sell'] = 1
return dataframe
class StrategyHelper:
"""
simple helper class to predefine a couple of patterns for our
strategy
"""
@staticmethod
def seven_green_candles(dataframe):
"""
evaluates if we are having 7 green candles in a row
:param self:
:param dataframe:
:return:
"""
return (
(dataframe['open'] < dataframe['close']) &
(dataframe['open'].shift(1) < dataframe['close'].shift(1)) &
(dataframe['open'].shift(2) < dataframe['close'].shift(2)) &
(dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
(dataframe['open'].shift(4) < dataframe['close'].shift(4)) &
(dataframe['open'].shift(5) < dataframe['close'].shift(5)) &
(dataframe['open'].shift(6) < dataframe['close'].shift(6)) &
(dataframe['open'].shift(7) < dataframe['close'].shift(7))
)
@staticmethod
def eight_green_candles(dataframe):
"""
evaluates if we are having 8 green candles in a row
:param self:
:param dataframe:
:return:
"""
return (
(dataframe['open'] < dataframe['close']) &
(dataframe['open'].shift(1) < dataframe['close'].shift(1)) &
(dataframe['open'].shift(2) < dataframe['close'].shift(2)) &
(dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
(dataframe['open'].shift(4) < dataframe['close'].shift(4)) &
(dataframe['open'].shift(5) < dataframe['close'].shift(5)) &
(dataframe['open'].shift(6) < dataframe['close'].shift(6)) &
(dataframe['open'].shift(7) < dataframe['close'].shift(7)) &
(dataframe['open'].shift(8) < dataframe['close'].shift(8))
)
@staticmethod
def eight_red_candles(dataframe, shift=0):
"""
evaluates if we are having 8 red candles in a row
:param self:
:param dataframe:
:param shift: shift the pattern by n
:return:
"""
return (
(dataframe['open'].shift(shift) > dataframe['close'].shift(shift)) &
(dataframe['open'].shift(1 + shift) > dataframe['close'].shift(1 + shift)) &
(dataframe['open'].shift(2 + shift) > dataframe['close'].shift(2 + shift)) &
(dataframe['open'].shift(3 + shift) > dataframe['close'].shift(3 + shift)) &
(dataframe['open'].shift(4 + shift) > dataframe['close'].shift(4 + shift)) &
(dataframe['open'].shift(5 + shift) > dataframe['close'].shift(5 + shift)) &
(dataframe['open'].shift(6 + shift) > dataframe['close'].shift(6 + shift)) &
(dataframe['open'].shift(7 + shift) > dataframe['close'].shift(7 + shift)) &
(dataframe['open'].shift(8 + shift) > dataframe['close'].shift(8 + shift))
)
@staticmethod
def four_green_one_red_candle(dataframe):
"""
evaluates if we are having a red candle and 4 previous green
:param self:
:param dataframe:
:return:
"""
return (
(dataframe['open'] > dataframe['close']) &
(dataframe['open'].shift(1) < dataframe['close'].shift(1)) &
(dataframe['open'].shift(2) < dataframe['close'].shift(2)) &
(dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
(dataframe['open'].shift(4) < dataframe['close'].shift(4))
)
@staticmethod
def four_red_one_green_candle(dataframe):
"""
evaluates if we are having a green candle and 4 previous red
:param self:
:param dataframe:
:return:
"""
return (
(dataframe['open'] < dataframe['close']) &
(dataframe['open'].shift(1) > dataframe['close'].shift(1)) &
(dataframe['open'].shift(2) > dataframe['close'].shift(2)) &
(dataframe['open'].shift(3) > dataframe['close'].shift(3)) &
(dataframe['open'].shift(4) > dataframe['close'].shift(4))
)

101
freqtrade-strategies-master/user_data/strategies/berlinguyinca/SmoothScalp.py Normal file
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# --- 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
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class SmoothScalp(IStrategy):
"""
this strategy is based around the idea of generating a lot of potentatils buys and make tiny profits on each trade
we recommend to have at least 60 parallel trades at any time to cover non avoidable losses
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.01
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
# should not be below 3% loss
stoploss = -0.5
# Optimal timeframe for the strategy
# the shorter the better
timeframe = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low')
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['adx'] = ta.ADX(dataframe)
dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['mfi'] = ta.MFI(dataframe)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['cci'] = ta.CCI(dataframe)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(dataframe['open'] < dataframe['ema_low']) &
(dataframe['adx'] > 30) &
(dataframe['mfi'] < 30) &
(
(dataframe['fastk'] < 30) &
(dataframe['fastd'] < 30) &
(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']))
) &
(dataframe['cci'] < -150)
)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(
(dataframe['open'] >= dataframe['ema_high'])
) |
(
(qtpylib.crossed_above(dataframe['fastk'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
)
) & (dataframe['cci'] > 150)
)
,
'sell'] = 1
return dataframe

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freqtrade-strategies-master/user_data/strategies/berlinguyinca/TDSequentialStrategy.py Normal file
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import talib.abstract as ta
from pandas import DataFrame
import scipy.signal
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy.interface import IStrategy
class TDSequentialStrategy(IStrategy):
"""
Strategy based on TD Sequential indicator.
source:
https://hackernoon.com/how-to-buy-sell-cryptocurrency-with-number-indicator-td-sequential-5af46f0ebce1
Buy trigger:
When you see 9 consecutive closes "lower" than the close 4 bars prior.
An ideal buy is when the low of bars 6 and 7 in the count are exceeded by the low of bars 8 or 9.
Sell trigger:
When you see 9 consecutive closes "higher" than the close 4 candles prior.
An ideal sell is when the the high of bars 6 and 7 in the count are exceeded by the high of bars 8 or 9.
Created by @bmoulkaf
"""
INTERFACE_VERSION = 2
# Minimal ROI designed for the strategy
minimal_roi = {'0': 5}
# Optimal stoploss designed for the strategy
stoploss = -0.05
# Trailing stoploss
trailing_stop = False
# trailing_only_offset_is_reached = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal timeframe for the strategy
timeframe = '1h'
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = False
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': False
}
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional time in force for orders
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc',
}
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)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
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.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
dataframe['exceed_high'] = False
dataframe['exceed_low'] = False
# count consecutive closes “lower” than the close 4 bars prior.
dataframe['seq_buy'] = dataframe['close'] < dataframe['close'].shift(4)
dataframe['seq_buy'] = dataframe['seq_buy'] * (dataframe['seq_buy'].groupby(
(dataframe['seq_buy'] != dataframe['seq_buy'].shift()).cumsum()).cumcount() + 1)
# count consecutive closes “higher” than the close 4 bars prior.
dataframe['seq_sell'] = dataframe['close'] > dataframe['close'].shift(4)
dataframe['seq_sell'] = dataframe['seq_sell'] * (dataframe['seq_sell'].groupby(
(dataframe['seq_sell'] != dataframe['seq_sell'].shift()).cumsum()).cumcount() + 1)
for index, row in dataframe.iterrows():
# check if the low of bars 6 and 7 in the count are exceeded by the low of bars 8 or 9.
seq_b = row['seq_buy']
if seq_b == 8:
dataframe.loc[index, 'exceed_low'] = (row['low'] < dataframe.loc[index - 2, 'low']) | \
(row['low'] < dataframe.loc[index - 1, 'low'])
if seq_b > 8:
dataframe.loc[index, 'exceed_low'] = (row['low'] < dataframe.loc[index - 3 - (seq_b - 9), 'low']) | \
(row['low'] < dataframe.loc[index - 2 - (seq_b - 9), 'low'])
if seq_b == 9:
dataframe.loc[index, 'exceed_low'] = row['exceed_low'] | dataframe.loc[index-1, 'exceed_low']
# check if the high of bars 6 and 7 in the count are exceeded by the high of bars 8 or 9.
seq_s = row['seq_sell']
if seq_s == 8:
dataframe.loc[index, 'exceed_high'] = (row['high'] > dataframe.loc[index - 2, 'high']) | \
(row['high'] > dataframe.loc[index - 1, 'high'])
if seq_s > 8:
dataframe.loc[index, 'exceed_high'] = (row['high'] > dataframe.loc[index - 3 - (seq_s - 9), 'high']) | \
(row['high'] > dataframe.loc[index - 2 - (seq_s - 9), 'high'])
if seq_s == 9:
dataframe.loc[index, 'exceed_high'] = row['exceed_high'] | dataframe.loc[index-1, 'exceed_high']
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
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe["buy"] = 0
dataframe.loc[((dataframe['exceed_low']) &
(dataframe['seq_buy'] > 8))
, '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
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy columnNA / NaN values
"""
dataframe["sell"] = 0
dataframe.loc[((dataframe['exceed_high']) |
(dataframe['seq_sell'] > 8))
, 'sell'] = 1
return dataframe

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freqtrade-strategies-master/user_data/strategies/berlinguyinca/TechnicalExampleStrategy.py Normal file
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from pandas import DataFrame
from technical.indicators import cmf
from freqtrade.strategy.interface import IStrategy
class TechnicalExampleStrategy(IStrategy):
minimal_roi = {
"0": 0.01
}
stoploss = -0.05
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['cmf'] = cmf(dataframe, 21)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(dataframe['cmf'] < 0)
)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# different strategy used for sell points, due to be able to duplicate it to 100%
dataframe.loc[
(
(dataframe['cmf'] > 0)
),
'sell'] = 1
return dataframe