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

freqtrade-strategies-master/.github/ISSUE_TEMPLATE.md

@@ -0,0 +1,17 @@
+*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://

freqtrade-strategies-master/.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,11 @@
+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>

freqtrade-strategies-master/.gitignore

@@ -0,0 +1,79 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
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+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
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+
+# Installer logs
+pip-log.txt
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+
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+
+# Flask stuff:
+instance/
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+
+# Scrapy stuff:
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+
+# Sphinx documentation
+docs/_build/
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+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+.env
+.venv
+.idea
+.vscode

freqtrade-strategies-master/LICENSE

@@ -0,0 +1,674 @@
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+
+  12. No Surrender of Others' Freedom.
+
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+
+                     END OF TERMS AND CONDITIONS
+
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+
+  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
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+
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+    Copyright (C) <year>  <name of author>
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+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+
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+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.
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+<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>.

freqtrade-strategies-master/README.md

@@ -0,0 +1,110 @@
+# 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.

freqtrade-strategies-master/user_data/hyperopts/AverageHyperopt.py

@@ -0,0 +1,154 @@
+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

freqtrade-strategies-master/user_data/hyperopts/MACDStrategy_hyperopt.py

@@ -0,0 +1,130 @@
+# 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

freqtrade-strategies-master/user_data/hyperopts/ReinforcedSmoothScalp_hyperopt.py

@@ -0,0 +1,195 @@
+# 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

freqtrade-strategies-master/user_data/strategies/InformativeSample.py

@@ -0,0 +1,128 @@
+
+# --- 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

freqtrade-strategies-master/user_data/strategies/Strategy001.py

@@ -0,0 +1,121 @@
+
+# --- 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

freqtrade-strategies-master/user_data/strategies/Strategy002.py

@@ -0,0 +1,135 @@
+
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from functools import reduce
+from pandas import DataFrame
+# --------------------------------
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+import numpy # noqa
+
+
+class Strategy002(IStrategy):
+    """
+    Strategy 002
+    author@: Gerald Lonlas
+    github@: https://github.com/freqtrade/freqtrade-strategies
+
+    How to use it?
+    > python3 ./freqtrade/main.py -s Strategy002
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "60":  0.01,
+        "30":  0.03,
+        "20":  0.04,
+        "0":  0.05
+    }
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.10
+
+    # Optimal timeframe for the strategy
+    timeframe = '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

freqtrade-strategies-master/user_data/strategies/Strategy003.py

@@ -0,0 +1,152 @@
+
+# --- 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

freqtrade-strategies-master/user_data/strategies/Strategy004.py

@@ -0,0 +1,154 @@
+
+# --- 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

freqtrade-strategies-master/user_data/strategies/Strategy005.py

@@ -0,0 +1,158 @@
+
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/ADXMomentum.py

@@ -0,0 +1,68 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/ASDTSRockwellTrading.py

@@ -0,0 +1,85 @@
+
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/AdxSmas.py

@@ -0,0 +1,60 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/AverageStrategy.py

@@ -0,0 +1,64 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/AwesomeMacd.py

@@ -0,0 +1,66 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/BbandRsi.py

@@ -0,0 +1,63 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/BinHV27.py

@@ -0,0 +1,135 @@
+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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/BinHV45.py

@@ -0,0 +1,57 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/CCIStrategy.py

@@ -0,0 +1,119 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/CMCWinner.py

@@ -0,0 +1,95 @@
+
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/ClucMay72018.py

@@ -0,0 +1,83 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/CofiBitStrategy.py

@@ -0,0 +1,80 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/CombinedBinHAndCluc.py

@@ -0,0 +1,75 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/DoesNothingStrategy.py

@@ -0,0 +1,44 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/EMASkipPump.py

@@ -0,0 +1,85 @@
+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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/Freqtrade_backtest_validation_freqtrade1.py

@@ -0,0 +1,49 @@
+# 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/Low_BB.py

@@ -0,0 +1,108 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/MACDStrategy.py

@@ -0,0 +1,83 @@
+
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/MACDStrategy_crossed.py

@@ -0,0 +1,77 @@
+
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/MultiRSI.py

@@ -0,0 +1,70 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/Quickie.py

@@ -0,0 +1,77 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedAverageStrategy.py

@@ -0,0 +1,96 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedQuickie.py

@@ -0,0 +1,194 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedSmoothScalp.py

@@ -0,0 +1,102 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/Scalp.py

@@ -0,0 +1,78 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/Simple.py

@@ -0,0 +1,75 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/SmoothOperator.py

@@ -0,0 +1,303 @@
+# --- 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))
+        )

freqtrade-strategies-master/user_data/strategies/berlinguyinca/SmoothScalp.py

@@ -0,0 +1,101 @@
+# --- 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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/TDSequentialStrategy.py

@@ -0,0 +1,151 @@
+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

freqtrade-strategies-master/user_data/strategies/berlinguyinca/TechnicalExampleStrategy.py

@@ -0,0 +1,40 @@
+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