From b57035bafb338675771e3b1c0c8b7826eab9862d Mon Sep 17 00:00:00 2001 From: region76 <77344960+region76@users.noreply.github.com> Date: Wed, 13 Jan 2021 13:59:13 +0300 Subject: [PATCH] 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 --- .../.github/ISSUE_TEMPLATE.md | 17 + .../.github/PULL_REQUEST_TEMPLATE.md | 11 + freqtrade-strategies-master/.gitignore | 79 ++ freqtrade-strategies-master/LICENSE | 674 ++++++++++++++++++ freqtrade-strategies-master/README.md | 110 +++ .../user_data/hyperopts/AverageHyperopt.py | 154 ++++ .../hyperopts/MACDStrategy_hyperopt.py | 130 ++++ .../ReinforcedSmoothScalp_hyperopt.py | 195 +++++ .../user_data/strategies/InformativeSample.py | 128 ++++ .../user_data/strategies/Strategy001.py | 121 ++++ .../user_data/strategies/Strategy002.py | 135 ++++ .../user_data/strategies/Strategy003.py | 152 ++++ .../user_data/strategies/Strategy004.py | 154 ++++ .../user_data/strategies/Strategy005.py | 158 ++++ .../strategies/berlinguyinca/ADXMomentum.py | 68 ++ .../berlinguyinca/ASDTSRockwellTrading.py | 85 +++ .../strategies/berlinguyinca/AdxSmas.py | 60 ++ .../berlinguyinca/AverageStrategy.py | 64 ++ .../strategies/berlinguyinca/AwesomeMacd.py | 66 ++ .../strategies/berlinguyinca/BbandRsi.py | 63 ++ .../strategies/berlinguyinca/BinHV27.py | 135 ++++ .../strategies/berlinguyinca/BinHV45.py | 57 ++ .../strategies/berlinguyinca/CCIStrategy.py | 119 ++++ .../strategies/berlinguyinca/CMCWinner.py | 95 +++ .../strategies/berlinguyinca/ClucMay72018.py | 83 +++ .../berlinguyinca/CofiBitStrategy.py | 80 +++ .../berlinguyinca/CombinedBinHAndCluc.py | 75 ++ .../berlinguyinca/DoesNothingStrategy.py | 44 ++ .../strategies/berlinguyinca/EMASkipPump.py | 85 +++ ...reqtrade_backtest_validation_freqtrade1.py | 49 ++ .../strategies/berlinguyinca/Low_BB.py | 108 +++ .../strategies/berlinguyinca/MACDStrategy.py | 83 +++ .../berlinguyinca/MACDStrategy_crossed.py | 77 ++ .../strategies/berlinguyinca/MultiRSI.py | 70 ++ .../strategies/berlinguyinca/Quickie.py | 77 ++ .../ReinforcedAverageStrategy.py | 96 +++ .../berlinguyinca/ReinforcedQuickie.py | 194 +++++ .../berlinguyinca/ReinforcedSmoothScalp.py | 102 +++ .../strategies/berlinguyinca/Scalp.py | 78 ++ .../strategies/berlinguyinca/Simple.py | 75 ++ .../berlinguyinca/SmoothOperator.py | 303 ++++++++ .../strategies/berlinguyinca/SmoothScalp.py | 101 +++ .../berlinguyinca/TDSequentialStrategy.py | 151 ++++ .../berlinguyinca/TechnicalExampleStrategy.py | 40 ++ 44 files changed, 5001 insertions(+) create mode 100644 freqtrade-strategies-master/.github/ISSUE_TEMPLATE.md create mode 100644 freqtrade-strategies-master/.github/PULL_REQUEST_TEMPLATE.md create mode 100644 freqtrade-strategies-master/.gitignore create mode 100644 freqtrade-strategies-master/LICENSE create mode 100644 freqtrade-strategies-master/README.md create mode 100644 freqtrade-strategies-master/user_data/hyperopts/AverageHyperopt.py create mode 100644 freqtrade-strategies-master/user_data/hyperopts/MACDStrategy_hyperopt.py create mode 100644 freqtrade-strategies-master/user_data/hyperopts/ReinforcedSmoothScalp_hyperopt.py create mode 100644 freqtrade-strategies-master/user_data/strategies/InformativeSample.py create mode 100644 freqtrade-strategies-master/user_data/strategies/Strategy001.py create mode 100644 freqtrade-strategies-master/user_data/strategies/Strategy002.py create mode 100644 freqtrade-strategies-master/user_data/strategies/Strategy003.py create mode 100644 freqtrade-strategies-master/user_data/strategies/Strategy004.py create mode 100644 freqtrade-strategies-master/user_data/strategies/Strategy005.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/ADXMomentum.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/ASDTSRockwellTrading.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/AdxSmas.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/AverageStrategy.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/AwesomeMacd.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/BbandRsi.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/BinHV27.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/BinHV45.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/CCIStrategy.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/CMCWinner.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/ClucMay72018.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/CofiBitStrategy.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/CombinedBinHAndCluc.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/DoesNothingStrategy.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/EMASkipPump.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/Freqtrade_backtest_validation_freqtrade1.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/Low_BB.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/MACDStrategy.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/MACDStrategy_crossed.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/MultiRSI.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/Quickie.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedAverageStrategy.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedQuickie.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedSmoothScalp.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/Scalp.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/Simple.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/SmoothOperator.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/SmoothScalp.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/TDSequentialStrategy.py create mode 100644 freqtrade-strategies-master/user_data/strategies/berlinguyinca/TechnicalExampleStrategy.py diff --git a/freqtrade-strategies-master/.github/ISSUE_TEMPLATE.md b/freqtrade-strategies-master/.github/ISSUE_TEMPLATE.md new file mode 100644 index 000000000..ae6090b9a --- /dev/null +++ b/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:// diff --git a/freqtrade-strategies-master/.github/PULL_REQUEST_TEMPLATE.md b/freqtrade-strategies-master/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 000000000..539e24be0 --- /dev/null +++ b/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 + +- +- diff --git a/freqtrade-strategies-master/.gitignore b/freqtrade-strategies-master/.gitignore new file mode 100644 index 000000000..bfd74c7e5 --- /dev/null +++ b/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/ +eggs/ +.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 +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +.env +.venv +.idea +.vscode diff --git a/freqtrade-strategies-master/LICENSE b/freqtrade-strategies-master/LICENSE new file mode 100644 index 000000000..94a9ed024 --- /dev/null +++ b/freqtrade-strategies-master/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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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 +. diff --git a/freqtrade-strategies-master/README.md b/freqtrade-strategies-master/README.md new file mode 100644 index 000000000..93966f80d --- /dev/null +++ b/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 ` (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. diff --git a/freqtrade-strategies-master/user_data/hyperopts/AverageHyperopt.py b/freqtrade-strategies-master/user_data/hyperopts/AverageHyperopt.py new file mode 100644 index 000000000..75b5f95a7 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/hyperopts/MACDStrategy_hyperopt.py b/freqtrade-strategies-master/user_data/hyperopts/MACDStrategy_hyperopt.py new file mode 100644 index 000000000..473e9c6b5 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/hyperopts/ReinforcedSmoothScalp_hyperopt.py b/freqtrade-strategies-master/user_data/hyperopts/ReinforcedSmoothScalp_hyperopt.py new file mode 100644 index 000000000..fcdffa942 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/InformativeSample.py b/freqtrade-strategies-master/user_data/strategies/InformativeSample.py new file mode 100644 index 000000000..7e81fd6b8 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/Strategy001.py b/freqtrade-strategies-master/user_data/strategies/Strategy001.py new file mode 100644 index 000000000..7b8bb77ce --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/Strategy002.py b/freqtrade-strategies-master/user_data/strategies/Strategy002.py new file mode 100644 index 000000000..aaaa94087 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/Strategy003.py b/freqtrade-strategies-master/user_data/strategies/Strategy003.py new file mode 100644 index 000000000..8d8630b77 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/Strategy004.py b/freqtrade-strategies-master/user_data/strategies/Strategy004.py new file mode 100644 index 000000000..22678065e --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/Strategy005.py b/freqtrade-strategies-master/user_data/strategies/Strategy005.py new file mode 100644 index 000000000..e79af99f2 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/ADXMomentum.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/ADXMomentum.py new file mode 100644 index 000000000..be0db98d1 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/ASDTSRockwellTrading.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/ASDTSRockwellTrading.py new file mode 100644 index 000000000..9cdfb6360 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/AdxSmas.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/AdxSmas.py new file mode 100644 index 000000000..7c8bfc9f5 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/AverageStrategy.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/AverageStrategy.py new file mode 100644 index 000000000..b0343654c --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/AwesomeMacd.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/AwesomeMacd.py new file mode 100644 index 000000000..f68f2eb4c --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/BbandRsi.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/BbandRsi.py new file mode 100644 index 000000000..6db3bc325 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/BinHV27.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/BinHV27.py new file mode 100644 index 000000000..eadad0d47 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/BinHV45.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/BinHV45.py new file mode 100644 index 000000000..958a3b36b --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/CCIStrategy.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/CCIStrategy.py new file mode 100644 index 000000000..921bbc199 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/CMCWinner.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/CMCWinner.py new file mode 100644 index 000000000..4364d37e1 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/ClucMay72018.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/ClucMay72018.py new file mode 100644 index 000000000..1ad6314ec --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/CofiBitStrategy.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/CofiBitStrategy.py new file mode 100644 index 000000000..40581396b --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/CombinedBinHAndCluc.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/CombinedBinHAndCluc.py new file mode 100644 index 000000000..22f903f6e --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/DoesNothingStrategy.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/DoesNothingStrategy.py new file mode 100644 index 000000000..05db7e228 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/EMASkipPump.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/EMASkipPump.py new file mode 100644 index 000000000..a2171616b --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/Freqtrade_backtest_validation_freqtrade1.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/Freqtrade_backtest_validation_freqtrade1.py new file mode 100644 index 000000000..31af28cf9 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/Low_BB.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/Low_BB.py new file mode 100644 index 000000000..4cc733b87 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/MACDStrategy.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/MACDStrategy.py new file mode 100644 index 000000000..c2ec4ee9d --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/MACDStrategy_crossed.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/MACDStrategy_crossed.py new file mode 100644 index 000000000..8bd882e59 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/MultiRSI.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/MultiRSI.py new file mode 100644 index 000000000..1465e4f85 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/Quickie.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/Quickie.py new file mode 100644 index 000000000..d4e017f06 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedAverageStrategy.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedAverageStrategy.py new file mode 100644 index 000000000..a1d32b43b --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedQuickie.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedQuickie.py new file mode 100644 index 000000000..2dccbc8f0 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedSmoothScalp.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/ReinforcedSmoothScalp.py new file mode 100644 index 000000000..a3bbe79a9 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/Scalp.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/Scalp.py new file mode 100644 index 000000000..fe4bc6743 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/Simple.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/Simple.py new file mode 100644 index 000000000..9f5318209 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/SmoothOperator.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/SmoothOperator.py new file mode 100644 index 000000000..dc06819ae --- /dev/null +++ b/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)) + ) diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/SmoothScalp.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/SmoothScalp.py new file mode 100644 index 000000000..2f5faabe4 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/TDSequentialStrategy.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/TDSequentialStrategy.py new file mode 100644 index 000000000..347732eb5 --- /dev/null +++ b/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 diff --git a/freqtrade-strategies-master/user_data/strategies/berlinguyinca/TechnicalExampleStrategy.py b/freqtrade-strategies-master/user_data/strategies/berlinguyinca/TechnicalExampleStrategy.py new file mode 100644 index 000000000..64df6f518 --- /dev/null +++ b/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