merging develop into async. requirement.txt conflict resolved
This commit is contained in:
misagh
commit
13ffd88053
@ -22,12 +22,13 @@ The table below will list all configuration parameters.
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| `stake_amount` | 0.05 | Yes | Amount of crypto-currency your bot will use for each trade. Per default, the bot will use (0.05 BTC x 3) = 0.15 BTC in total will be always engaged. Set it to 'unlimited' to allow the bot to use all avaliable balance.
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| `ticker_interval` | [1m, 5m, 30m, 1h, 1d] | No | The ticker interval to use (1min, 5 min, 30 min, 1 hour or 1 day). Default is 5 minutes
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| `fiat_display_currency` | USD | Yes | Fiat currency used to show your profits. More information below.
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| `dry_run` | true | Yes | Define if the bot must be in Dry-run or production mode.
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| `dry_run` | true | Yes | Define if the bot must be in Dry-run or production mode.
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| `process_only_new_candles` | false | No | If set to true indicators are processed only once a new candle arrives. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. Can be set either in Configuration or in the strategy.
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| `minimal_roi` | See below | No | Set the threshold in percent the bot will use to sell a trade. More information below. If set, this parameter will override `minimal_roi` from your strategy file.
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| `stoploss` | -0.10 | No | Value of the stoploss in percent used by the bot. More information below. If set, this parameter will override `stoploss` from your strategy file.
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| `trailing_stoploss` | false | No | Enables trailing stop-loss (based on `stoploss` in either configuration or strategy file).
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| `trailing_stoploss_positve` | 0 | No | Changes stop-loss once profit has been reached.
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| `trailing_stoploss_positve_offset` | 0 | No | Offset on when to apply `trailing_stoploss_positive`. Percentage value which should be positive.
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| `trailing_stop` | false | No | Enables trailing stop-loss (based on `stoploss` in either configuration or strategy file).
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| `trailing_stop_positve` | 0 | No | Changes stop-loss once profit has been reached.
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| `trailing_stop_positve_offset` | 0 | No | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive.
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| `unfilledtimeout.buy` | 10 | Yes | How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled.
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| `unfilledtimeout.sell` | 10 | Yes | How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled.
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| `bid_strategy.ask_last_balance` | 0.0 | Yes | Set the bidding price. More information below.
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@ -8,7 +8,6 @@ To understand how to set up the bot please read the [Bot Configuration](https://
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* [Table of Contents](#table-of-contents)
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* [Easy Installation - Linux Script](#easy-installation---linux-script)
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* [Manual installation](#manual-installation)
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* [Automatic Installation - Docker](#automatic-installation---docker)
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* [Custom Linux MacOS Installation](#custom-installation)
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- [Requirements](#requirements)
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@ -56,34 +55,6 @@ Reset parameter will hard reset your branch (only if you are on `master` or `dev
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Config parameter is a `config.json` configurator. This script will ask you questions to setup your bot and create your `config.json`.
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## Manual installation - Linux/MacOS
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The following steps are made for Linux/MacOS environment
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### 1. Clone the repo
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```bash
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git clone git@github.com:freqtrade/freqtrade.git
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git checkout develop
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cd freqtrade
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```
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### 2. Create the config file
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Switch `"dry_run": true,`
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```bash
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cp config.json.example config.json
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vi config.json
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```
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### 3. Build your docker image and run it
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```bash
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docker build -t freqtrade .
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docker run --rm -v /etc/localtime:/etc/localtime:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
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```
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------
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## Automatic Installation - Docker
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@ -196,7 +167,7 @@ docker run -d \
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freqtrade --db-url sqlite:///tradesv3.sqlite
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```
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NOTE: db-url defaults to `sqlite:///tradesv3.sqlite` but it defaults to `sqlite://` if `dry_run=True` is being used.
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*Note*: db-url defaults to `sqlite:///tradesv3.sqlite` but it defaults to `sqlite://` if `dry_run=True` is being used.
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To override this behaviour use a custom db-url value: i.e.: `--db-url sqlite:///tradesv3.dryrun.sqlite`
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### 6. Monitor your Docker instance
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@ -211,14 +182,15 @@ docker stop freqtrade
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docker start freqtrade
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```
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You do not need to rebuild the image for configuration changes, it will suffice to edit `config.json` and restart the container.
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For more information on how to operate Docker, please refer to the [official Docker documentation](https://docs.docker.com/).
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*Note*: You do not need to rebuild the image for configuration changes, it will suffice to edit `config.json` and restart the container.
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### 7. Backtest with docker
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The following assumes that the above steps (1-4) have been completed successfully.
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Also, backtest-data should be available at `~/.freqtrade/user_data/`.
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``` bash
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docker run -d \
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--name freqtrade \
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@ -238,12 +210,13 @@ Head over to the [Backtesting Documentation](https://github.com/freqtrade/freqtr
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## Custom Installation
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We've included/collected install instructions for Ubuntu 16.04, MacOS, and Windows. These are guidelines and your success may vary with other distros.
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OS Specific steps are listed first, the [common](#common) section below is necessary for all systems.
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### Requirements
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Click each one for install guide:
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* [Python 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/), note the bot was not tested on Python >= 3.7.x
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* [Python >= 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/)
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* [pip](https://pip.pypa.io/en/stable/installing/)
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* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
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* [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
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@ -251,7 +224,7 @@ Click each one for install guide:
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### Linux - Ubuntu 16.04
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#### 1. Install Python 3.6, Git, and wget
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#### Install Python 3.6, Git, and wget
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```bash
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sudo add-apt-repository ppa:jonathonf/python-3.6
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@ -259,7 +232,34 @@ sudo apt-get update
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sudo apt-get install python3.6 python3.6-venv python3.6-dev build-essential autoconf libtool pkg-config make wget git
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```
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#### 2. Install TA-Lib
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#### Raspberry Pi / Raspbian
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Before installing FreqTrade on a Raspberry Pi running the official Raspbian Image, make sure you have at least Python 3.6 installed. The default image only provides Python 3.5. Probably the easiest way to get a recent version of python is [miniconda](https://repo.continuum.io/miniconda/).
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The following assumes that miniconda3 is installed and available in your environment, and is installed.
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It's recommended to use (mini)conda for this as installation/compilation of `scipy` and `pandas` takes a long time.
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``` bash
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conda config --add channels rpi
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conda install python=3.6
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conda create -n freqtrade python=3.6
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conda install scipy pandas
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pip install -r requirements.txt
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pip install -e .
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```
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### MacOS
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#### Install Python 3.6, git, wget and ta-lib
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```bash
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brew install python3 git wget
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```
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### common
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#### 1. Install TA-Lib
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Official webpage: https://mrjbq7.github.io/ta-lib/install.html
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@ -275,15 +275,60 @@ cd ..
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rm -rf ./ta-lib*
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```
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*Note*: An already downloaded version of ta-lib is included in the repository, as the sourceforge.net source seems to have problems frequently.
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#### 2. Setup your Python virtual environment (virtualenv)
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*Note*: This step is optional but strongly recommended to keep your system organized
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```bash
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python3 -m venv .env
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source .env/bin/activate
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```
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#### 3. Install FreqTrade
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Clone the git repository:
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```bash
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git clone https://github.com/freqtrade/freqtrade.git
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```
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#### 4. Configure `freqtrade` as a `systemd` service
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Optionally checkout the stable/master branch:
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```bash
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git checkout master
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```
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#### 4. Initialize the configuration
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```bash
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cd freqtrade
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cp config.json.example config.json
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```
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> *To edit the config please refer to [Bot Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md).*
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#### 5. Install python dependencies
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``` bash
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pip3 install --upgrade pip
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pip3 install -r requirements.txt
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pip3 install -e .
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```
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#### 6. Run the Bot
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If this is the first time you run the bot, ensure you are running it in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
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```bash
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python3.6 ./freqtrade/main.py -c config.json
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```
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*Note*: If you run the bot on a server, you should consider using [Docker](#automatic-installation---docker) a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
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#### 7. [Optional] Configure `freqtrade` as a `systemd` service
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From the freqtrade repo... copy `freqtrade.service` to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
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@ -299,57 +344,6 @@ For this to be persistent (run when user is logged out) you'll need to enable `l
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sudo loginctl enable-linger "$USER"
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```
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### MacOS
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#### 1. Install Python 3.6, git, wget and ta-lib
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```bash
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brew install python3 git wget ta-lib
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```
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#### 2. Install FreqTrade
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Clone the git repository:
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```bash
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git clone https://github.com/freqtrade/freqtrade.git
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```
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Optionally checkout the develop branch:
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```bash
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git checkout develop
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```
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### Setup Config and virtual env
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#### 1. Initialize the configuration
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```bash
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cd freqtrade
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cp config.json.example config.json
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```
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> *To edit the config please refer to [Bot Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md).*
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#### 2. Setup your Python virtual environment (virtualenv)
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```bash
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python3.6 -m venv .env
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source .env/bin/activate
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pip3.6 install --upgrade pip
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pip3.6 install -r requirements.txt
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pip3.6 install -e .
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```
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#### 3. Run the Bot
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If this is the first time you run the bot, ensure you are running it in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
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```bash
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python3.6 ./freqtrade/main.py -c config.json
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```
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------
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## Windows
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@ -369,7 +363,7 @@ git clone https://github.com/freqtrade/freqtrade.git
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copy paste `config.json` to ``\path\freqtrade-develop\freqtrade`
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#### install ta-lib
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#### Install ta-lib
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Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
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@ -390,5 +384,17 @@ REM >pip install TA_Lib‑0.4.17‑cp36‑cp36m‑win32.whl
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> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/freqtrade/freqtrade/issues/222)
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#### Error during installation under Windows
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``` bash
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error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
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```
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Unfortunately, many packages requiring compilation don't provide a pre-build wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
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The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building c code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or docker first.
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---
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Now you have an environment ready, the next step is
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[Bot Configuration](https://github.com/freqtrade/freqtrade/blob/develop/docs/configuration.md)...
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@ -53,6 +53,7 @@ CONF_SCHEMA = {
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},
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'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
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'dry_run': {'type': 'boolean'},
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'process_only_new_candles': {'type': 'boolean'},
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'minimal_roi': {
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'type': 'object',
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'patternProperties': {
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@ -70,8 +70,15 @@ class IStrategy(ABC):
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# associated ticker interval
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ticker_interval: str
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# run "populate_indicators" only for new candle
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process_only_new_candles: bool = False
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# Dict to determine if analysis is necessary
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_last_candle_seen_per_pair: Dict[str, datetime] = {}
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def __init__(self, config: dict) -> None:
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self.config = config
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self._last_candle_seen_per_pair = {}
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@abstractmethod
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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@ -112,10 +119,30 @@ class IStrategy(ABC):
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add several TA indicators and buy signal to it
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:return DataFrame with ticker data and indicator data
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"""
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dataframe = parse_ticker_dataframe(ticker_history)
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dataframe = self.advise_indicators(dataframe, metadata)
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dataframe = self.advise_buy(dataframe, metadata)
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dataframe = self.advise_sell(dataframe, metadata)
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pair = str(metadata.get('pair'))
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# Test if seen this pair and last candle before.
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# always run if process_only_new_candles is set to true
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if (not self.process_only_new_candles or
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self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
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# Defs that only make change on new candle data.
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logging.debug("TA Analysis Launched")
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dataframe = self.advise_indicators(dataframe, metadata)
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dataframe = self.advise_buy(dataframe, metadata)
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dataframe = self.advise_sell(dataframe, metadata)
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self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
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else:
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logging.debug("Skippinig TA Analysis for already analyzed candle")
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dataframe['buy'] = 0
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dataframe['sell'] = 0
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# Other Defs in strategy that want to be called every loop here
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# twitter_sell = self.watch_twitter_feed(dataframe, metadata)
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logging.debug("Loop Analysis Launched")
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return dataframe
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def get_signal(self, pair: str, interval: str,
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@ -66,6 +66,15 @@ class StrategyResolver(object):
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else:
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config['ticker_interval'] = self.strategy.ticker_interval
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if 'process_only_new_candles' in config:
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self.strategy.process_only_new_candles = config['process_only_new_candles']
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logger.info(
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"Override process_only_new_candles 'process_only_new_candles' "
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"with value in config file: %s.", config['process_only_new_candles']
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)
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else:
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config['process_only_new_candles'] = self.strategy.process_only_new_candles
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# Sort and apply type conversions
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self.strategy.minimal_roi = OrderedDict(sorted(
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{int(key): value for (key, value) in self.strategy.minimal_roi.items()}.items(),
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@ -128,3 +128,75 @@ def test_min_roi_reached(default_conf, fee) -> None:
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|
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assert not strategy.min_roi_reached(trade, -0.01, arrow.utcnow().shift(minutes=-1).datetime)
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assert strategy.min_roi_reached(trade, 0.02, arrow.utcnow().shift(minutes=-1).datetime)
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def test_analyze_ticker_default(ticker_history, mocker, caplog) -> None:
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caplog.set_level(logging.DEBUG)
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ind_mock = MagicMock(side_effect=lambda x, meta: x)
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buy_mock = MagicMock(side_effect=lambda x, meta: x)
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sell_mock = MagicMock(side_effect=lambda x, meta: x)
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mocker.patch.multiple(
|
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'freqtrade.strategy.interface.IStrategy',
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advise_indicators=ind_mock,
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advise_buy=buy_mock,
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advise_sell=sell_mock,
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||||
|
||||
)
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strategy = DefaultStrategy({})
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strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
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assert ind_mock.call_count == 1
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assert buy_mock.call_count == 1
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assert buy_mock.call_count == 1
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assert log_has('TA Analysis Launched', caplog.record_tuples)
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assert not log_has('Skippinig TA Analysis for already analyzed candle',
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caplog.record_tuples)
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caplog.clear()
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strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
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# No analysis happens as process_only_new_candles is true
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assert ind_mock.call_count == 2
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assert buy_mock.call_count == 2
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||||
assert buy_mock.call_count == 2
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assert log_has('TA Analysis Launched', caplog.record_tuples)
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assert not log_has('Skippinig TA Analysis for already analyzed candle',
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caplog.record_tuples)
|
||||
|
||||
|
||||
def test_analyze_ticker_skip_analyze(ticker_history, mocker, caplog) -> None:
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caplog.set_level(logging.DEBUG)
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ind_mock = MagicMock(side_effect=lambda x, meta: x)
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buy_mock = MagicMock(side_effect=lambda x, meta: x)
|
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sell_mock = MagicMock(side_effect=lambda x, meta: x)
|
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mocker.patch.multiple(
|
||||
'freqtrade.strategy.interface.IStrategy',
|
||||
advise_indicators=ind_mock,
|
||||
advise_buy=buy_mock,
|
||||
advise_sell=sell_mock,
|
||||
|
||||
)
|
||||
strategy = DefaultStrategy({})
|
||||
strategy.process_only_new_candles = True
|
||||
|
||||
ret = strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
|
||||
assert ind_mock.call_count == 1
|
||||
assert buy_mock.call_count == 1
|
||||
assert buy_mock.call_count == 1
|
||||
assert log_has('TA Analysis Launched', caplog.record_tuples)
|
||||
assert not log_has('Skippinig TA Analysis for already analyzed candle',
|
||||
caplog.record_tuples)
|
||||
caplog.clear()
|
||||
|
||||
ret = strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
|
||||
# No analysis happens as process_only_new_candles is true
|
||||
assert ind_mock.call_count == 1
|
||||
assert buy_mock.call_count == 1
|
||||
assert buy_mock.call_count == 1
|
||||
# only skipped analyze adds buy and sell columns, otherwise it's all mocked
|
||||
assert 'buy' in ret
|
||||
assert 'sell' in ret
|
||||
assert ret['buy'].sum() == 0
|
||||
assert ret['sell'].sum() == 0
|
||||
assert not log_has('TA Analysis Launched', caplog.record_tuples)
|
||||
assert log_has('Skippinig TA Analysis for already analyzed candle',
|
||||
caplog.record_tuples)
|
||||
|
||||
@ -165,6 +165,23 @@ def test_strategy_override_ticker_interval(caplog):
|
||||
) in caplog.record_tuples
|
||||
|
||||
|
||||
def test_strategy_override_process_only_new_candles(caplog):
|
||||
caplog.set_level(logging.INFO)
|
||||
|
||||
config = {
|
||||
'strategy': 'DefaultStrategy',
|
||||
'process_only_new_candles': True
|
||||
}
|
||||
resolver = StrategyResolver(config)
|
||||
|
||||
assert resolver.strategy.process_only_new_candles
|
||||
assert ('freqtrade.strategy.resolver',
|
||||
logging.INFO,
|
||||
"Override process_only_new_candles 'process_only_new_candles' "
|
||||
"with value in config file: True."
|
||||
) in caplog.record_tuples
|
||||
|
||||
|
||||
def test_deprecate_populate_indicators(result):
|
||||
default_location = path.join(path.dirname(path.realpath(__file__)))
|
||||
resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
ccxt==1.17.205
|
||||
ccxt==1.17.233
|
||||
SQLAlchemy==1.2.11
|
||||
python-telegram-bot==10.1.0
|
||||
python-telegram-bot==11.1.0
|
||||
arrow==0.12.1
|
||||
cachetools==2.1.0
|
||||
requests==2.19.1
|
||||
@ -12,10 +12,10 @@ scipy==1.1.0
|
||||
jsonschema==2.6.0
|
||||
numpy==1.15.1
|
||||
TA-Lib==0.4.17
|
||||
pytest==3.7.3
|
||||
pytest==3.7.4
|
||||
pytest-mock==1.10.0
|
||||
pytest-cov==2.5.1
|
||||
pytest-asyncio==0.9.0
|
||||
pytest-cov==2.6.0
|
||||
tabulate==0.8.2
|
||||
coinmarketcap==5.0.3
|
||||
|
||||
|
||||
@ -45,6 +45,9 @@ class TestStrategy(IStrategy):
|
||||
# Optimal ticker interval for the strategy
|
||||
ticker_interval = '5m'
|
||||
|
||||
# run "populate_indicators" only for new candle
|
||||
ta_on_candle = False
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Adds several different TA indicators to the given DataFrame
|
||||
|
||||
Loading…
Reference in New Issue
Block a user