Merge branch 'develop' into align_userdata
This commit is contained in:
Matthias
4
.gitignore
vendored
4
.gitignore
vendored
@@ -81,7 +81,6 @@ target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
*.ipynb
|
||||
|
||||
# pyenv
|
||||
.python-version
|
||||
@@ -93,3 +92,6 @@ target/
|
||||
|
||||
.pytest_cache/
|
||||
.mypy_cache/
|
||||
|
||||
#exceptions
|
||||
!user_data/noteboks/*example.ipynb
|
||||
|
||||
14
.travis.yml
14
.travis.yml
@@ -10,15 +10,11 @@ services:
|
||||
env:
|
||||
global:
|
||||
- IMAGE_NAME=freqtradeorg/freqtrade
|
||||
addons:
|
||||
apt:
|
||||
packages:
|
||||
- libelf-dev
|
||||
- libdw-dev
|
||||
- binutils-dev
|
||||
install:
|
||||
- cd build_helpers && ./install_ta-lib.sh; cd ..
|
||||
- export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
|
||||
- cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
|
||||
- export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
|
||||
- export TA_LIBRARY_PATH=${HOME}/dependencies/lib
|
||||
- export TA_INCLUDE_PATH=${HOME}/dependencies/lib/include
|
||||
- pip install -r requirements-dev.txt
|
||||
- pip install -e .
|
||||
jobs:
|
||||
@@ -55,4 +51,4 @@ notifications:
|
||||
cache:
|
||||
pip: True
|
||||
directories:
|
||||
- /usr/local/lib/
|
||||
- $HOME/dependencies
|
||||
|
||||
@@ -1,8 +1,14 @@
|
||||
if [ ! -f "/usr/local/lib/libta_lib.a" ]; then
|
||||
if [ -z "$1" ]; then
|
||||
INSTALL_LOC=/usr/local
|
||||
else
|
||||
INSTALL_LOC=${1}
|
||||
fi
|
||||
echo "Installing to ${INSTALL_LOC}"
|
||||
if [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
|
||||
tar zxvf ta-lib-0.4.0-src.tar.gz
|
||||
cd ta-lib \
|
||||
&& sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \
|
||||
&& ./configure \
|
||||
&& ./configure --prefix=${INSTALL_LOC}/ \
|
||||
&& make \
|
||||
&& which sudo && sudo make install || make install \
|
||||
&& cd ..
|
||||
|
||||
@@ -57,7 +57,15 @@ freqtrade backtesting --datadir freqtrade/tests/testdata-20180101
|
||||
freqtrade -s TestStrategy backtesting
|
||||
```
|
||||
|
||||
Where `-s TestStrategy` refers to the class name within the strategy file `test_strategy.py` found in the `freqtrade/user_data/strategies` directory
|
||||
Where `-s TestStrategy` refers to the class name within the strategy file `test_strategy.py` found in the `freqtrade/user_data/strategies` directory.
|
||||
|
||||
#### Comparing multiple Strategies
|
||||
|
||||
```bash
|
||||
freqtrade backtesting --strategy-list TestStrategy1 AwesomeStrategy --ticker-interval 5m
|
||||
```
|
||||
|
||||
Where `TestStrategy1` and `AwesomeStrategy` refer to class names of strategies.
|
||||
|
||||
#### Exporting trades to file
|
||||
|
||||
|
||||
@@ -193,7 +193,7 @@ optional arguments:
|
||||
number).
|
||||
-l, --live Use live data.
|
||||
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
|
||||
Provide a commaseparated list of strategies to
|
||||
Provide a space-separated list of strategies to
|
||||
backtest Please note that ticker-interval needs to be
|
||||
set either in config or via command line. When using
|
||||
this together with --export trades, the strategy-name
|
||||
|
||||
@@ -1,164 +1,114 @@
|
||||
# Analyzing bot data
|
||||
|
||||
After performing backtests, or after running the bot for some time, it will be interesting to analyze the results your bot generated.
|
||||
You can analyze the results of backtests and trading history easily using Jupyter notebooks. A sample notebook is located at `user_data/notebooks/analysis_example.ipynb`. For usage instructions, see [jupyter.org](https://jupyter.org/documentation).
|
||||
|
||||
A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.
|
||||
*Pro tip - Don't forget to start a jupyter notbook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
|
||||
|
||||
The following helpers will help you loading the data into Pandas DataFrames, and may also give you some starting points in analyzing the results.
|
||||
## Example snippets
|
||||
|
||||
## Strategy development problem analysis
|
||||
|
||||
Debugging a strategy (are there no buy signals, ...) can be very time-consuming.
|
||||
FreqTrade tries to help you by exposing a few helper-functions, which can be very handy.
|
||||
|
||||
It's recommended using Juptyer Notebooks for analysis, since it offers a dynamic way to rerun certain parts of the code.
|
||||
|
||||
The following is a full code-snippet, which will be explained by both comments, and step by step below.
|
||||
### Load backtest results into a pandas dataframe
|
||||
|
||||
```python
|
||||
# Some necessary imports
|
||||
from pathlib import Path
|
||||
|
||||
from freqtrade.data.history import load_pair_history
|
||||
from freqtrade.resolvers import StrategyResolver
|
||||
# Define some constants
|
||||
ticker_interval = "5m"
|
||||
|
||||
# Name of the strategy class
|
||||
strategyname = 'Awesomestrategy'
|
||||
# Location of the strategy
|
||||
strategy_location = '../xmatt/strategies'
|
||||
# Location of the data
|
||||
data_location = '../freqtrade/user_data/data/binance/'
|
||||
# Only use one pair here
|
||||
pair = "XRP_ETH"
|
||||
|
||||
### End constants
|
||||
|
||||
# Load data
|
||||
bt_data = load_pair_history(datadir=Path(data_location),
|
||||
ticker_interval = ticker_interval,
|
||||
pair=pair)
|
||||
print(len(bt_data))
|
||||
|
||||
### Start strategy reload
|
||||
# Load strategy - best done in a new cell
|
||||
# Rerun each time the strategy-file is changed.
|
||||
strategy = StrategyResolver({'strategy': strategyname,
|
||||
'user_data_dir': Path.cwd(),
|
||||
'strategy_path': location}).strategy
|
||||
|
||||
# Run strategy (just like in backtesting)
|
||||
df = strategy.analyze_ticker(bt_data, {'pair': pair})
|
||||
print(f"Generated {df['buy'].sum()} buy signals")
|
||||
|
||||
# Reindex data to be "nicer" and show data
|
||||
data = df.set_index('date', drop=True)
|
||||
data.tail()
|
||||
|
||||
```
|
||||
|
||||
### Explanation
|
||||
|
||||
#### Imports and constant definition
|
||||
|
||||
``` python
|
||||
# Some necessary imports
|
||||
from pathlib import Path
|
||||
|
||||
from freqtrade.data.history import load_pair_history
|
||||
from freqtrade.resolvers import StrategyResolver
|
||||
# Define some constants
|
||||
ticker_interval = "5m"
|
||||
|
||||
# Name of the strategy class
|
||||
strategyname = 'Awesomestrategy'
|
||||
# Location of the strategy
|
||||
strategy_location = 'user_data/strategies'
|
||||
# Location of the data
|
||||
data_location = 'user_data/data/binance'
|
||||
# Only use one pair here
|
||||
pair = "XRP_ETH"
|
||||
```
|
||||
|
||||
This first section imports necessary modules, and defines some constants you'll probably need to adjust for your case.
|
||||
|
||||
#### Load candles
|
||||
|
||||
``` python
|
||||
# Load data
|
||||
bt_data = load_pair_history(datadir=Path(data_location),
|
||||
ticker_interval = ticker_interval,
|
||||
pair=pair)
|
||||
print(len(bt_data))
|
||||
```
|
||||
|
||||
This second section loads the historic data and prints the amount of candles in the DataFrame.
|
||||
You can also inspect this dataframe by using `bt_data.head()` or `bt_data.tail()`.
|
||||
|
||||
#### Run strategy and analyze results
|
||||
|
||||
Now, it's time to load and run your strategy.
|
||||
For this, I recommend using a new cell in your notebook, since you'll want to repeat this until you're satisfied with your strategy.
|
||||
|
||||
``` python
|
||||
# Load strategy - best done in a new cell
|
||||
# Needs to be ran each time the strategy-file is changed.
|
||||
strategy = StrategyResolver({'strategy': strategyname,
|
||||
'user_data_dir': Path.cwd(),
|
||||
'strategy_path': location}).strategy
|
||||
|
||||
# Run strategy (just like in backtesting)
|
||||
df = strategy.analyze_ticker(bt_data, {'pair': pair})
|
||||
print(f"Generated {df['buy'].sum()} buy signals")
|
||||
|
||||
# Reindex data to be "nicer" and show data
|
||||
data = df.set_index('date', drop=True)
|
||||
data.tail()
|
||||
```
|
||||
|
||||
The code snippet loads and analyzes the strategy, calculates and prints the number of buy signals.
|
||||
|
||||
The last 2 lines serve to analyze the dataframe in detail.
|
||||
This can be important if your strategy did not generate any buy signals.
|
||||
Note that using `data.head()` would also work, however this is misleading since most indicators have some "startup" time at the start of a backtested dataframe.
|
||||
|
||||
There can be many things wrong, some signs to look for are:
|
||||
|
||||
* Columns with NaN values at the end of the dataframe
|
||||
* Columns used in `crossed*()` functions with completely different units
|
||||
|
||||
## Backtesting
|
||||
|
||||
To analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
|
||||
You can then load the trades to perform further analysis.
|
||||
|
||||
Freqtrade provides the `load_backtest_data()` helper function to easily load the backtest results, which takes the path to the the backtest-results file as parameter.
|
||||
|
||||
``` python
|
||||
from freqtrade.data.btanalysis import load_backtest_data
|
||||
# Load backtest results
|
||||
df = load_backtest_data("user_data/backtest_results/backtest-result.json")
|
||||
|
||||
# Show value-counts per pair
|
||||
df.groupby("pair")["sell_reason"].value_counts()
|
||||
|
||||
```
|
||||
|
||||
This will allow you to drill deeper into your backtest results, and perform analysis which otherwise would make the regular backtest-output very difficult to digest due to information overload.
|
||||
|
||||
If you have some ideas for interesting / helpful backtest data analysis ideas, please submit a Pull Request so the community can benefit from it.
|
||||
|
||||
## Live data
|
||||
|
||||
To analyze the trades your bot generated, you can load them to a DataFrame as follows:
|
||||
### Load live trading results into a pandas dataframe
|
||||
|
||||
``` python
|
||||
from freqtrade.data.btanalysis import load_trades_from_db
|
||||
|
||||
# Fetch trades from database
|
||||
df = load_trades_from_db("sqlite:///tradesv3.sqlite")
|
||||
|
||||
# Display results
|
||||
df.groupby("pair")["sell_reason"].value_counts()
|
||||
```
|
||||
|
||||
## Strategy debugging example
|
||||
|
||||
Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
|
||||
|
||||
### Import requirements and define variables used in analyses
|
||||
|
||||
```python
|
||||
# Imports
|
||||
from pathlib import Path
|
||||
import os
|
||||
from freqtrade.data.history import load_pair_history
|
||||
from freqtrade.resolvers import StrategyResolver
|
||||
|
||||
# You can override strategy settings as demonstrated below.
|
||||
# Customize these according to your needs.
|
||||
|
||||
# Define some constants
|
||||
ticker_interval = "5m"
|
||||
# Name of the strategy class
|
||||
strategy_name = 'AwesomeStrategy'
|
||||
# Path to user data
|
||||
user_data_dir = 'user_data'
|
||||
# Location of the strategy
|
||||
strategy_location = Path(user_data_dir, 'strategies')
|
||||
# Location of the data
|
||||
data_location = Path(user_data_dir, 'data', 'binance')
|
||||
# Pair to analyze
|
||||
# Only use one pair here
|
||||
pair = "BTC_USDT"
|
||||
```
|
||||
|
||||
### Load exchange data
|
||||
|
||||
```python
|
||||
# Load data using values set above
|
||||
bt_data = load_pair_history(datadir=Path(data_location),
|
||||
ticker_interval=ticker_interval,
|
||||
pair=pair)
|
||||
|
||||
# Confirm success
|
||||
print(f"Loaded {len(bt_data)} rows of data for {pair} from {data_location}")
|
||||
```
|
||||
|
||||
### Load and run strategy
|
||||
|
||||
* Rerun each time the strategy file is changed
|
||||
|
||||
```python
|
||||
# Load strategy using values set above
|
||||
strategy = StrategyResolver({'strategy': strategy_name,
|
||||
'user_data_dir': user_data_dir,
|
||||
'strategy_path': strategy_location}).strategy
|
||||
|
||||
# Generate buy/sell signals using strategy
|
||||
df = strategy.analyze_ticker(bt_data, {'pair': pair})
|
||||
```
|
||||
|
||||
### Display the trade details
|
||||
|
||||
* Note that using `data.head()` would also work, however most indicators have some "startup" data at the top of the dataframe.
|
||||
|
||||
#### Some possible problems
|
||||
|
||||
* Columns with NaN values at the end of the dataframe
|
||||
* Columns used in `crossed*()` functions with completely different units
|
||||
|
||||
#### Comparison with full backtest
|
||||
|
||||
having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.
|
||||
|
||||
Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns > 29).
|
||||
The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available.
|
||||
|
||||
```python
|
||||
# Report results
|
||||
print(f"Generated {df['buy'].sum()} buy signals")
|
||||
data = df.set_index('date', drop=True)
|
||||
data.tail()
|
||||
```
|
||||
|
||||
Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
|
||||
|
||||
@@ -18,19 +18,24 @@ Configuring hyperopt is similar to writing your own strategy, and many tasks wil
|
||||
|
||||
### Checklist on all tasks / possibilities in hyperopt
|
||||
|
||||
Depending on the space you want to optimize, only some of the below are required.
|
||||
Depending on the space you want to optimize, only some of the below are required:
|
||||
|
||||
* fill `populate_indicators` - probably a copy from your strategy
|
||||
* fill `buy_strategy_generator` - for buy signal optimization
|
||||
* fill `indicator_space` - for buy signal optimzation
|
||||
* fill `sell_strategy_generator` - for sell signal optimization
|
||||
* fill `sell_indicator_space` - for sell signal optimzation
|
||||
* fill `roi_space` - for ROI optimization
|
||||
* fill `generate_roi_table` - for ROI optimization (if you need more than 3 entries)
|
||||
* fill `stoploss_space` - stoploss optimization
|
||||
* Optional but recommended
|
||||
* copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
|
||||
* copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
|
||||
|
||||
Optional, but recommended:
|
||||
|
||||
* copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
|
||||
* copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
|
||||
|
||||
Rarely you may also need to override:
|
||||
|
||||
* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
|
||||
* `generate_roi_table` - for custom ROI optimization (if you need more than 4 entries in the ROI table)
|
||||
* `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
|
||||
|
||||
### 1. Install a Custom Hyperopt File
|
||||
|
||||
@@ -345,7 +350,7 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
|
||||
|
||||
### Understand Hyperopt ROI results
|
||||
|
||||
If you are optimizing ROI, you're result will look as follows and include a ROI table.
|
||||
If you are optimizing ROI (i.e. if optimization search-space contains 'all' or 'roi'), your result will look as follows and include a ROI table:
|
||||
|
||||
```
|
||||
Best result:
|
||||
@@ -376,6 +381,41 @@ minimal_roi = {
|
||||
}
|
||||
```
|
||||
|
||||
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps) with the values that can vary in the following ranges:
|
||||
|
||||
| # | minutes | ROI percentage |
|
||||
|---|---|---|
|
||||
| 1 | always 0 | 0.03...0.31 |
|
||||
| 2 | 10...40 | 0.02...0.11 |
|
||||
| 3 | 20...100 | 0.01...0.04 |
|
||||
| 4 | 30...220 | always 0 |
|
||||
|
||||
This structure of the ROI table is sufficient in most cases. Override the `roi_space()` method defining the ranges desired if you need components of the ROI tables to vary in other ranges.
|
||||
|
||||
Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization in these methods if you need a different structure of the ROI table or other amount of rows (steps) in the ROI tables.
|
||||
|
||||
### Understand Hyperopt Stoploss results
|
||||
|
||||
If you are optimizing stoploss values (i.e. if optimization search-space contains 'all' or 'stoploss'), your result will look as follows and include stoploss:
|
||||
|
||||
```
|
||||
Best result:
|
||||
|
||||
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
|
||||
|
||||
Buy hyperspace params:
|
||||
{ 'adx-value': 44,
|
||||
'rsi-value': 29,
|
||||
'adx-enabled': False,
|
||||
'rsi-enabled': True,
|
||||
'trigger': 'bb_lower'}
|
||||
Stoploss: -0.37996664668703606
|
||||
```
|
||||
|
||||
If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace can vary in the range -0.5...-0.02, which is sufficient in most cases.
|
||||
|
||||
Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization.
|
||||
|
||||
### Validate backtesting results
|
||||
|
||||
Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
|
||||
|
||||
@@ -219,6 +219,17 @@ as the watchdog.
|
||||
|
||||
------
|
||||
|
||||
## Using Conda
|
||||
|
||||
Freqtrade can also be installed using Anaconda (or Miniconda).
|
||||
|
||||
``` bash
|
||||
conda env create -f environment.yml
|
||||
```
|
||||
|
||||
!!! Note:
|
||||
This requires the [ta-lib](#1-install-ta-lib) C-library to be installed first.
|
||||
|
||||
## Windows
|
||||
|
||||
We recommend that Windows users use [Docker](docker.md) as this will work much easier and smoother (also more secure).
|
||||
|
||||
@@ -1 +1 @@
|
||||
mkdocs-material==3.1.0
|
||||
mkdocs-material==4.4.0
|
||||
59
environment.yml
Normal file
59
environment.yml
Normal file
@@ -0,0 +1,59 @@
|
||||
name: freqtrade
|
||||
channels:
|
||||
- defaults
|
||||
- conda-forge
|
||||
dependencies:
|
||||
# Required for app
|
||||
- python>=3.6
|
||||
- pip
|
||||
- wheel
|
||||
- numpy
|
||||
- pandas
|
||||
- scipy
|
||||
- SQLAlchemy
|
||||
- scikit-learn
|
||||
- arrow
|
||||
- requests
|
||||
- urllib3
|
||||
- wrapt
|
||||
- joblib
|
||||
- jsonschema
|
||||
- tabulate
|
||||
- python-rapidjson
|
||||
- filelock
|
||||
- flask
|
||||
- python-dotenv
|
||||
- cachetools
|
||||
- scikit-optimize
|
||||
- python-telegram-bot
|
||||
# Optional for plotting
|
||||
- plotly
|
||||
# Optional for development
|
||||
- flake8
|
||||
- pytest
|
||||
- pytest-mock
|
||||
- pytest-asyncio
|
||||
- pytest-cov
|
||||
- coveralls
|
||||
- mypy
|
||||
# Useful for jupyter
|
||||
- jupyter
|
||||
- ipykernel
|
||||
- isort
|
||||
- yapf
|
||||
- pip:
|
||||
# Required for app
|
||||
- cython
|
||||
- coinmarketcap
|
||||
- ccxt
|
||||
- TA-Lib
|
||||
- py_find_1st
|
||||
- sdnotify
|
||||
# Optional for develpment
|
||||
- flake8-tidy-imports
|
||||
- flake8-type-annotations
|
||||
- pytest-random-order
|
||||
- -e .
|
||||
|
||||
|
||||
|
||||
@@ -135,7 +135,7 @@ AVAILABLE_CLI_OPTIONS = {
|
||||
),
|
||||
"strategy_list": Arg(
|
||||
'--strategy-list',
|
||||
help='Provide a comma-separated list of strategies to backtest. '
|
||||
help='Provide a space-separated list of strategies to backtest. '
|
||||
'Please note that ticker-interval needs to be set either in config '
|
||||
'or via command line. When using this together with `--export trades`, '
|
||||
'the strategy-name is injected into the filename '
|
||||
|
||||
@@ -1,9 +1,7 @@
|
||||
"""
|
||||
This module contains the configuration class
|
||||
"""
|
||||
import json
|
||||
import logging
|
||||
import sys
|
||||
import warnings
|
||||
from argparse import Namespace
|
||||
from pathlib import Path
|
||||
@@ -13,6 +11,7 @@ from freqtrade import OperationalException, constants
|
||||
from freqtrade.configuration.check_exchange import check_exchange
|
||||
from freqtrade.configuration.directory_operations import create_datadir, create_userdata_dir
|
||||
from freqtrade.configuration.json_schema import validate_config_schema
|
||||
from freqtrade.configuration.load_config import load_config_file
|
||||
from freqtrade.loggers import setup_logging
|
||||
from freqtrade.misc import deep_merge_dicts
|
||||
from freqtrade.state import RunMode
|
||||
@@ -53,24 +52,7 @@ class Configuration(object):
|
||||
logger.info('Using config: %s ...', path)
|
||||
|
||||
# Merge config options, overwriting old values
|
||||
config = deep_merge_dicts(self._load_config_file(path), config)
|
||||
|
||||
return config
|
||||
|
||||
def _load_config_file(self, path: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Loads a config file from the given path
|
||||
:param path: path as str
|
||||
:return: configuration as dictionary
|
||||
"""
|
||||
try:
|
||||
# Read config from stdin if requested in the options
|
||||
with open(path) if path != '-' else sys.stdin as file:
|
||||
config = json.load(file)
|
||||
except FileNotFoundError:
|
||||
raise OperationalException(
|
||||
f'Config file "{path}" not found!'
|
||||
' Please create a config file or check whether it exists.')
|
||||
config = deep_merge_dicts(load_config_file(path), config)
|
||||
|
||||
return config
|
||||
|
||||
|
||||
30
freqtrade/configuration/load_config.py
Normal file
30
freqtrade/configuration/load_config.py
Normal file
@@ -0,0 +1,30 @@
|
||||
"""
|
||||
This module contain functions to load the configuration file
|
||||
"""
|
||||
import json
|
||||
import logging
|
||||
import sys
|
||||
from typing import Any, Dict
|
||||
|
||||
from freqtrade import OperationalException
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def load_config_file(path: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Loads a config file from the given path
|
||||
:param path: path as str
|
||||
:return: configuration as dictionary
|
||||
"""
|
||||
try:
|
||||
# Read config from stdin if requested in the options
|
||||
with open(path) if path != '-' else sys.stdin as file:
|
||||
config = json.load(file)
|
||||
except FileNotFoundError:
|
||||
raise OperationalException(
|
||||
f'Config file "{path}" not found!'
|
||||
' Please create a config file or check whether it exists.')
|
||||
|
||||
return config
|
||||
@@ -725,7 +725,8 @@ class Exchange(object):
|
||||
return []
|
||||
try:
|
||||
# Allow 5s offset to catch slight time offsets (discovered in #1185)
|
||||
my_trades = self._api.fetch_my_trades(pair, since.timestamp() - 5)
|
||||
# since needs to be int in milliseconds
|
||||
my_trades = self._api.fetch_my_trades(pair, int((since.timestamp() - 5) * 1000))
|
||||
matched_trades = [trade for trade in my_trades if trade['order'] == order_id]
|
||||
|
||||
return matched_trades
|
||||
|
||||
@@ -10,8 +10,8 @@ from pathlib import Path
|
||||
from typing import Any, Dict, List, NamedTuple, Optional
|
||||
|
||||
from pandas import DataFrame
|
||||
from tabulate import tabulate
|
||||
|
||||
from freqtrade import OperationalException
|
||||
from freqtrade.configuration import Arguments
|
||||
from freqtrade.data import history
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
@@ -21,6 +21,7 @@ from freqtrade.persistence import Trade
|
||||
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
|
||||
from freqtrade.state import RunMode
|
||||
from freqtrade.strategy.interface import IStrategy, SellType
|
||||
from tabulate import tabulate
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -88,6 +89,9 @@ class Backtesting(object):
|
||||
Load strategy into backtesting
|
||||
"""
|
||||
self.strategy = strategy
|
||||
if "ticker_interval" not in self.config:
|
||||
raise OperationalException("Ticker-interval needs to be set in either configuration "
|
||||
"or as cli argument `--ticker-interval 5m`")
|
||||
|
||||
self.ticker_interval = self.config.get('ticker_interval')
|
||||
self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
|
||||
@@ -373,7 +377,9 @@ class Backtesting(object):
|
||||
continue
|
||||
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
|
||||
|
||||
trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]:],
|
||||
# since indexes has been incremented before, we need to go one step back to
|
||||
# also check the buying candle for sell conditions.
|
||||
trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]-1:],
|
||||
trade_count_lock, stake_amount,
|
||||
max_open_trades)
|
||||
|
||||
|
||||
@@ -5,7 +5,7 @@ from typing import Any, Callable, Dict, List
|
||||
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
from skopt.space import Categorical, Dimension, Integer, Real
|
||||
from skopt.space import Categorical, Dimension, Integer
|
||||
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
from freqtrade.optimize.hyperopt_interface import IHyperOpt
|
||||
@@ -13,10 +13,9 @@ from freqtrade.optimize.hyperopt_interface import IHyperOpt
|
||||
|
||||
class DefaultHyperOpts(IHyperOpt):
|
||||
"""
|
||||
Default hyperopt provided by freqtrade bot.
|
||||
Default hyperopt provided by the Freqtrade bot.
|
||||
You can override it with your own hyperopt
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe['adx'] = ta.ADX(dataframe)
|
||||
@@ -156,42 +155,6 @@ class DefaultHyperOpts(IHyperOpt):
|
||||
'sell-sar_reversal'], name='sell-trigger')
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def generate_roi_table(params: Dict) -> Dict[int, float]:
|
||||
"""
|
||||
Generate the ROI table that will be used by Hyperopt
|
||||
"""
|
||||
roi_table = {}
|
||||
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
|
||||
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
|
||||
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
|
||||
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
|
||||
|
||||
return roi_table
|
||||
|
||||
@staticmethod
|
||||
def stoploss_space() -> List[Dimension]:
|
||||
"""
|
||||
Stoploss Value to search
|
||||
"""
|
||||
return [
|
||||
Real(-0.5, -0.02, name='stoploss'),
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def roi_space() -> List[Dimension]:
|
||||
"""
|
||||
Values to search for each ROI steps
|
||||
"""
|
||||
return [
|
||||
Integer(10, 120, name='roi_t1'),
|
||||
Integer(10, 60, name='roi_t2'),
|
||||
Integer(10, 40, name='roi_t3'),
|
||||
Real(0.01, 0.04, name='roi_p1'),
|
||||
Real(0.01, 0.07, name='roi_p2'),
|
||||
Real(0.01, 0.20, name='roi_p3'),
|
||||
]
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators. Should be a copy of from strategy
|
||||
|
||||
@@ -7,7 +7,7 @@ from abc import ABC, abstractmethod
|
||||
from typing import Dict, Any, Callable, List
|
||||
|
||||
from pandas import DataFrame
|
||||
from skopt.space import Dimension
|
||||
from skopt.space import Dimension, Integer, Real
|
||||
|
||||
|
||||
class IHyperOpt(ABC):
|
||||
@@ -26,56 +26,80 @@ class IHyperOpt(ABC):
|
||||
@abstractmethod
|
||||
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Populate indicators that will be used in the Buy and Sell strategy
|
||||
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
|
||||
:return: a Dataframe with all mandatory indicators for the strategies
|
||||
Populate indicators that will be used in the Buy and Sell strategy.
|
||||
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe().
|
||||
:return: A Dataframe with all mandatory indicators for the strategies.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
@abstractmethod
|
||||
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
|
||||
"""
|
||||
Create a buy strategy generator
|
||||
Create a buy strategy generator.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
@abstractmethod
|
||||
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
|
||||
"""
|
||||
Create a sell strategy generator
|
||||
Create a sell strategy generator.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
@abstractmethod
|
||||
def indicator_space() -> List[Dimension]:
|
||||
"""
|
||||
Create an indicator space
|
||||
Create an indicator space.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
@abstractmethod
|
||||
def sell_indicator_space() -> List[Dimension]:
|
||||
"""
|
||||
Create a sell indicator space
|
||||
Create a sell indicator space.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
@abstractmethod
|
||||
def generate_roi_table(params: Dict) -> Dict[int, float]:
|
||||
"""
|
||||
Create an roi table
|
||||
Create a ROI table.
|
||||
|
||||
Generates the ROI table that will be used by Hyperopt.
|
||||
You may override it in your custom Hyperopt class.
|
||||
"""
|
||||
roi_table = {}
|
||||
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
|
||||
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
|
||||
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
|
||||
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
|
||||
|
||||
return roi_table
|
||||
|
||||
@staticmethod
|
||||
@abstractmethod
|
||||
def stoploss_space() -> List[Dimension]:
|
||||
"""
|
||||
Create a stoploss space
|
||||
Create a stoploss space.
|
||||
|
||||
Defines range of stoploss values to search.
|
||||
You may override it in your custom Hyperopt class.
|
||||
"""
|
||||
return [
|
||||
Real(-0.5, -0.02, name='stoploss'),
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
@abstractmethod
|
||||
def roi_space() -> List[Dimension]:
|
||||
"""
|
||||
Create a roi space
|
||||
Create a ROI space.
|
||||
|
||||
Defines values to search for each ROI steps.
|
||||
You may override it in your custom Hyperopt class.
|
||||
"""
|
||||
return [
|
||||
Integer(10, 120, name='roi_t1'),
|
||||
Integer(10, 60, name='roi_t2'),
|
||||
Integer(10, 40, name='roi_t3'),
|
||||
Real(0.01, 0.04, name='roi_p1'),
|
||||
Real(0.01, 0.07, name='roi_p2'),
|
||||
Real(0.01, 0.20, name='roi_p3'),
|
||||
]
|
||||
|
||||
@@ -39,7 +39,7 @@ class SharpeHyperOptLoss(IHyperOptLoss):
|
||||
sharp_ratio = expected_yearly_return / np.std(total_profit) * np.sqrt(365)
|
||||
else:
|
||||
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
|
||||
sharp_ratio = 20.
|
||||
sharp_ratio = -20.
|
||||
|
||||
# print(expected_yearly_return, np.std(total_profit), sharp_ratio)
|
||||
return -sharp_ratio
|
||||
|
||||
@@ -45,7 +45,7 @@ def get_args(args):
|
||||
|
||||
def patched_configuration_load_config_file(mocker, config) -> None:
|
||||
mocker.patch(
|
||||
'freqtrade.configuration.configuration.Configuration._load_config_file',
|
||||
'freqtrade.configuration.configuration.load_config_file',
|
||||
lambda *args, **kwargs: config
|
||||
)
|
||||
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
# pragma pylint: disable=protected-access
|
||||
import copy
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from datetime import datetime, timezone
|
||||
from random import randint
|
||||
from unittest.mock import MagicMock, Mock, PropertyMock
|
||||
|
||||
@@ -11,8 +11,8 @@ import ccxt
|
||||
import pytest
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade import (DependencyException, OperationalException,
|
||||
TemporaryError, InvalidOrderException)
|
||||
from freqtrade import (DependencyException, InvalidOrderException,
|
||||
OperationalException, TemporaryError)
|
||||
from freqtrade.exchange import Binance, Exchange, Kraken
|
||||
from freqtrade.exchange.exchange import API_RETRY_COUNT
|
||||
from freqtrade.resolvers.exchange_resolver import ExchangeResolver
|
||||
@@ -1361,7 +1361,7 @@ def test_name(default_conf, mocker, exchange_name):
|
||||
@pytest.mark.parametrize("exchange_name", EXCHANGES)
|
||||
def test_get_trades_for_order(default_conf, mocker, exchange_name):
|
||||
order_id = 'ABCD-ABCD'
|
||||
since = datetime(2018, 5, 5)
|
||||
since = datetime(2018, 5, 5, tzinfo=timezone.utc)
|
||||
default_conf["dry_run"] = False
|
||||
mocker.patch('freqtrade.exchange.Exchange.exchange_has', return_value=True)
|
||||
api_mock = MagicMock()
|
||||
@@ -1391,6 +1391,13 @@ def test_get_trades_for_order(default_conf, mocker, exchange_name):
|
||||
orders = exchange.get_trades_for_order(order_id, 'LTC/BTC', since)
|
||||
assert len(orders) == 1
|
||||
assert orders[0]['price'] == 165
|
||||
assert api_mock.fetch_my_trades.call_count == 1
|
||||
# since argument should be
|
||||
assert isinstance(api_mock.fetch_my_trades.call_args[0][1], int)
|
||||
assert api_mock.fetch_my_trades.call_args[0][0] == 'LTC/BTC'
|
||||
# Same test twice, hardcoded number and doing the same calculation
|
||||
assert api_mock.fetch_my_trades.call_args[0][1] == 1525478395000
|
||||
assert api_mock.fetch_my_trades.call_args[0][1] == int(since.timestamp() - 5) * 1000
|
||||
|
||||
ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name,
|
||||
'get_trades_for_order', 'fetch_my_trades',
|
||||
|
||||
@@ -14,9 +14,8 @@ from freqtrade.tests.optimize import (BTContainer, BTrade,
|
||||
_get_frame_time_from_offset,
|
||||
tests_ticker_interval)
|
||||
|
||||
# Test 0 Sell signal sell
|
||||
# Test 0: Sell with signal sell in candle 3
|
||||
# Test with Stop-loss at 1%
|
||||
# TC0: Sell signal in candle 3
|
||||
tc0 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
|
||||
@@ -29,9 +28,8 @@ tc0 = BTContainer(data=[
|
||||
trades=[BTrade(sell_reason=SellType.SELL_SIGNAL, open_tick=1, close_tick=4)]
|
||||
)
|
||||
|
||||
# Test 1 Minus 8% Close
|
||||
# Test 1: Stop-Loss Triggered 1% loss
|
||||
# Test with Stop-loss at 1%
|
||||
# TC1: Stop-Loss Triggered 1% loss
|
||||
tc1 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
|
||||
@@ -45,9 +43,8 @@ tc1 = BTContainer(data=[
|
||||
)
|
||||
|
||||
|
||||
# Test 2 Minus 4% Low, minus 1% close
|
||||
# Test 2: Minus 4% Low, minus 1% close
|
||||
# Test with Stop-Loss at 3%
|
||||
# TC2: Stop-Loss Triggered 3% Loss
|
||||
tc2 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
|
||||
@@ -61,11 +58,11 @@ tc2 = BTContainer(data=[
|
||||
)
|
||||
|
||||
|
||||
# Test 3 Candle drops 4%, Recovers 1%.
|
||||
# Test 3: Multiple trades.
|
||||
# Candle drops 4%, Recovers 1%.
|
||||
# Entry Criteria Met
|
||||
# Candle drops 20%
|
||||
# Test with Stop-Loss at 2%
|
||||
# TC3: Trade-A: Stop-Loss Triggered 2% Loss
|
||||
# Trade-A: Stop-Loss Triggered 2% Loss
|
||||
# Trade-B: Stop-Loss Triggered 2% Loss
|
||||
tc3 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
@@ -81,10 +78,10 @@ tc3 = BTContainer(data=[
|
||||
BTrade(sell_reason=SellType.STOP_LOSS, open_tick=4, close_tick=5)]
|
||||
)
|
||||
|
||||
# Test 4 Minus 3% / recovery +15%
|
||||
# Test 4: Minus 3% / recovery +15%
|
||||
# Candle Data for test 3 – Candle drops 3% Closed 15% up
|
||||
# Test with Stop-loss at 2% ROI 6%
|
||||
# TC4: Stop-Loss Triggered 2% Loss
|
||||
# Stop-Loss Triggered 2% Loss
|
||||
tc4 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
|
||||
@@ -97,9 +94,8 @@ tc4 = BTContainer(data=[
|
||||
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
|
||||
)
|
||||
|
||||
# Test 5 / Drops 0.5% Closes +20%
|
||||
# Set stop-loss at 1% ROI 3%
|
||||
# TC5: ROI triggers 3% Gain
|
||||
# Test 5: Drops 0.5% Closes +20%, ROI triggers 3% Gain
|
||||
# stop-loss: 1%, ROI: 3%
|
||||
tc5 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5025, 4980, 4987, 6172, 1, 0],
|
||||
@@ -112,9 +108,8 @@ tc5 = BTContainer(data=[
|
||||
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=3)]
|
||||
)
|
||||
|
||||
# Test 6 / Drops 3% / Recovers 6% Positive / Closes 1% positve
|
||||
# Set stop-loss at 2% ROI at 5%
|
||||
# TC6: Stop-Loss triggers 2% Loss
|
||||
# Test 6: Drops 3% / Recovers 6% Positive / Closes 1% positve, Stop-Loss triggers 2% Loss
|
||||
# stop-loss: 2% ROI: 5%
|
||||
tc6 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
|
||||
@@ -127,9 +122,8 @@ tc6 = BTContainer(data=[
|
||||
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
|
||||
)
|
||||
|
||||
# Test 7 - 6% Positive / 1% Negative / Close 1% Positve
|
||||
# Set stop-loss at 2% ROI at 3%
|
||||
# TC7: ROI Triggers 3% Gain
|
||||
# Test 7: 6% Positive / 1% Negative / Close 1% Positve, ROI Triggers 3% Gain
|
||||
# stop-loss: 2% ROI: 3%
|
||||
tc7 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
|
||||
@@ -143,9 +137,8 @@ tc7 = BTContainer(data=[
|
||||
)
|
||||
|
||||
|
||||
# Test 8 - trailing_stop should raise so candle 3 causes a stoploss.
|
||||
# Set stop-loss at 10%, ROI at 10% (should not apply)
|
||||
# TC8: Trailing stoploss - stoploss should be adjusted candle 2
|
||||
# Test 8: trailing_stop should raise so candle 3 causes a stoploss.
|
||||
# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted in candle 2
|
||||
tc8 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
|
||||
@@ -158,10 +151,8 @@ tc8 = BTContainer(data=[
|
||||
)
|
||||
|
||||
|
||||
# Test 9 - trailing_stop should raise - high and low in same candle.
|
||||
# Candle Data for test 9
|
||||
# Set stop-loss at 10%, ROI at 10% (should not apply)
|
||||
# TC9: Trailing stoploss - stoploss should be adjusted candle 3
|
||||
# Test 9: trailing_stop should raise - high and low in same candle.
|
||||
# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted in candle 3
|
||||
tc9 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
|
||||
@@ -173,10 +164,9 @@ tc9 = BTContainer(data=[
|
||||
trades=[BTrade(sell_reason=SellType.TRAILING_STOP_LOSS, open_tick=1, close_tick=3)]
|
||||
)
|
||||
|
||||
# Test 10 - trailing_stop should raise so candle 3 causes a stoploss
|
||||
# Test 10: trailing_stop should raise so candle 3 causes a stoploss
|
||||
# without applying trailing_stop_positive since stoploss_offset is at 10%.
|
||||
# Set stop-loss at 10%, ROI at 10% (should not apply)
|
||||
# TC10: Trailing stoploss - stoploss should be adjusted candle 2
|
||||
# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
|
||||
tc10 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
|
||||
@@ -190,10 +180,9 @@ tc10 = BTContainer(data=[
|
||||
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=4)]
|
||||
)
|
||||
|
||||
# Test 11 - trailing_stop should raise so candle 3 causes a stoploss
|
||||
# Test 11: trailing_stop should raise so candle 3 causes a stoploss
|
||||
# applying a positive trailing stop of 3% since stop_positive_offset is reached.
|
||||
# Set stop-loss at 10%, ROI at 10% (should not apply)
|
||||
# TC11: Trailing stoploss - stoploss should be adjusted candle 2,
|
||||
# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
|
||||
tc11 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
|
||||
@@ -207,10 +196,9 @@ tc11 = BTContainer(data=[
|
||||
trades=[BTrade(sell_reason=SellType.TRAILING_STOP_LOSS, open_tick=1, close_tick=3)]
|
||||
)
|
||||
|
||||
# Test 12 - trailing_stop should raise in candle 2 and cause a stoploss in the same candle
|
||||
# Test 12: trailing_stop should raise in candle 2 and cause a stoploss in the same candle
|
||||
# applying a positive trailing stop of 3% since stop_positive_offset is reached.
|
||||
# Set stop-loss at 10%, ROI at 10% (should not apply)
|
||||
# TC12: Trailing stoploss - stoploss should be adjusted candle 2,
|
||||
# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
|
||||
tc12 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
|
||||
@@ -224,6 +212,47 @@ tc12 = BTContainer(data=[
|
||||
trades=[BTrade(sell_reason=SellType.TRAILING_STOP_LOSS, open_tick=1, close_tick=2)]
|
||||
)
|
||||
|
||||
# Test 13: Buy and sell ROI on same candle
|
||||
# stop-loss: 10% (should not apply), ROI: 1%
|
||||
tc13 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
|
||||
[1, 5000, 5100, 4950, 5100, 6172, 0, 0],
|
||||
[2, 5100, 5251, 4850, 5100, 6172, 0, 0],
|
||||
[3, 4850, 5050, 4850, 4750, 6172, 0, 0],
|
||||
[4, 4750, 4950, 4850, 4750, 6172, 0, 0]],
|
||||
stop_loss=-0.10, roi=0.01, profit_perc=0.01,
|
||||
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=1)]
|
||||
)
|
||||
|
||||
# Test 14 - Buy and Stoploss on same candle
|
||||
# stop-loss: 5%, ROI: 10% (should not apply)
|
||||
tc14 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
|
||||
[1, 5000, 5100, 4600, 5100, 6172, 0, 0],
|
||||
[2, 5100, 5251, 4850, 5100, 6172, 0, 0],
|
||||
[3, 4850, 5050, 4850, 4750, 6172, 0, 0],
|
||||
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
|
||||
stop_loss=-0.05, roi=0.10, profit_perc=-0.05,
|
||||
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=1)]
|
||||
)
|
||||
|
||||
|
||||
# Test 15 - Buy and ROI on same candle, followed by buy and Stoploss on next candle
|
||||
# stop-loss: 5%, ROI: 10% (should not apply)
|
||||
tc15 = BTContainer(data=[
|
||||
# D O H L C V B S
|
||||
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
|
||||
[1, 5000, 5100, 4900, 5100, 6172, 1, 0],
|
||||
[2, 5100, 5251, 4650, 5100, 6172, 0, 0],
|
||||
[3, 4850, 5050, 4850, 4750, 6172, 0, 0],
|
||||
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
|
||||
stop_loss=-0.05, roi=0.01, profit_perc=-0.04,
|
||||
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=1),
|
||||
BTrade(sell_reason=SellType.STOP_LOSS, open_tick=2, close_tick=2)]
|
||||
)
|
||||
|
||||
TESTS = [
|
||||
tc0,
|
||||
tc1,
|
||||
@@ -238,6 +267,9 @@ TESTS = [
|
||||
tc10,
|
||||
tc11,
|
||||
tc12,
|
||||
tc13,
|
||||
tc14,
|
||||
tc15,
|
||||
]
|
||||
|
||||
|
||||
|
||||
@@ -9,7 +9,7 @@ import pandas as pd
|
||||
import pytest
|
||||
from arrow import Arrow
|
||||
|
||||
from freqtrade import DependencyException, constants
|
||||
from freqtrade import DependencyException, OperationalException, constants
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data import history
|
||||
from freqtrade.data.btanalysis import evaluate_result_multi
|
||||
@@ -21,7 +21,8 @@ from freqtrade.optimize.backtesting import Backtesting
|
||||
from freqtrade.state import RunMode
|
||||
from freqtrade.strategy.default_strategy import DefaultStrategy
|
||||
from freqtrade.strategy.interface import SellType
|
||||
from freqtrade.tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
|
||||
from freqtrade.tests.conftest import (get_args, log_has, log_has_re,
|
||||
patch_exchange,
|
||||
patched_configuration_load_config_file)
|
||||
|
||||
|
||||
@@ -345,6 +346,23 @@ def test_backtesting_init(mocker, default_conf, order_types) -> None:
|
||||
assert not backtesting.strategy.order_types["stoploss_on_exchange"]
|
||||
|
||||
|
||||
def test_backtesting_init_no_ticker_interval(mocker, default_conf, caplog) -> None:
|
||||
"""
|
||||
Check that stoploss_on_exchange is set to False while backtesting
|
||||
since backtesting assumes a perfect stoploss anyway.
|
||||
"""
|
||||
patch_exchange(mocker)
|
||||
del default_conf['ticker_interval']
|
||||
default_conf['strategy_list'] = ['DefaultStrategy',
|
||||
'TestStrategy']
|
||||
|
||||
mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.5))
|
||||
with pytest.raises(OperationalException):
|
||||
Backtesting(default_conf)
|
||||
log_has("Ticker-interval needs to be set in either configuration "
|
||||
"or as cli argument `--ticker-interval 5m`", caplog.record_tuples)
|
||||
|
||||
|
||||
def test_tickerdata_to_dataframe_bt(default_conf, mocker) -> None:
|
||||
patch_exchange(mocker)
|
||||
timerange = TimeRange(None, 'line', 0, -100)
|
||||
@@ -618,8 +636,9 @@ def test_processed(default_conf, mocker) -> None:
|
||||
|
||||
|
||||
def test_backtest_pricecontours(default_conf, fee, mocker) -> None:
|
||||
# TODO: Evaluate usefullness of this, the patterns and buy-signls are unrealistic
|
||||
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
|
||||
tests = [['raise', 19], ['lower', 0], ['sine', 18]]
|
||||
tests = [['raise', 19], ['lower', 0], ['sine', 35]]
|
||||
# We need to enable sell-signal - otherwise it sells on ROI!!
|
||||
default_conf['experimental'] = {"use_sell_signal": True}
|
||||
|
||||
|
||||
@@ -15,6 +15,7 @@ from freqtrade.configuration import Arguments, Configuration
|
||||
from freqtrade.configuration.check_exchange import check_exchange
|
||||
from freqtrade.configuration.directory_operations import create_datadir, create_userdata_dir
|
||||
from freqtrade.configuration.json_schema import validate_config_schema
|
||||
from freqtrade.configuration.load_config import load_config_file
|
||||
from freqtrade.constants import DEFAULT_DB_DRYRUN_URL, DEFAULT_DB_PROD_URL
|
||||
from freqtrade.loggers import _set_loggers
|
||||
from freqtrade.state import RunMode
|
||||
@@ -26,8 +27,7 @@ from freqtrade.tests.conftest import (log_has, log_has_re,
|
||||
def all_conf():
|
||||
config_file = Path(__file__).parents[2] / "config_full.json.example"
|
||||
print(config_file)
|
||||
configuration = Configuration(Namespace())
|
||||
conf = configuration._load_config_file(str(config_file))
|
||||
conf = load_config_file(str(config_file))
|
||||
return conf
|
||||
|
||||
|
||||
@@ -54,12 +54,11 @@ def test_load_config_incorrect_stake_amount(default_conf) -> None:
|
||||
|
||||
def test_load_config_file(default_conf, mocker, caplog) -> None:
|
||||
del default_conf['user_data_dir']
|
||||
file_mock = mocker.patch('freqtrade.configuration.configuration.open', mocker.mock_open(
|
||||
file_mock = mocker.patch('freqtrade.configuration.load_config.open', mocker.mock_open(
|
||||
read_data=json.dumps(default_conf)
|
||||
))
|
||||
|
||||
configuration = Configuration(Namespace())
|
||||
validated_conf = configuration._load_config_file('somefile')
|
||||
validated_conf = load_config_file('somefile')
|
||||
assert file_mock.call_count == 1
|
||||
assert validated_conf.items() >= default_conf.items()
|
||||
|
||||
@@ -115,7 +114,7 @@ def test_load_config_combine_dicts(default_conf, mocker, caplog) -> None:
|
||||
|
||||
configsmock = MagicMock(side_effect=config_files)
|
||||
mocker.patch(
|
||||
'freqtrade.configuration.configuration.Configuration._load_config_file',
|
||||
'freqtrade.configuration.configuration.load_config_file',
|
||||
configsmock
|
||||
)
|
||||
|
||||
@@ -155,10 +154,9 @@ def test_load_config_file_exception(mocker) -> None:
|
||||
'freqtrade.configuration.configuration.open',
|
||||
MagicMock(side_effect=FileNotFoundError('File not found'))
|
||||
)
|
||||
configuration = Configuration(Namespace())
|
||||
|
||||
with pytest.raises(OperationalException, match=r'.*Config file "somefile" not found!*'):
|
||||
configuration._load_config_file('somefile')
|
||||
load_config_file('somefile')
|
||||
|
||||
|
||||
def test_load_config(default_conf, mocker) -> None:
|
||||
|
||||
@@ -1,16 +1,16 @@
|
||||
# requirements without requirements installable via conda
|
||||
# mainly used for Raspberry pi installs
|
||||
ccxt==1.18.992
|
||||
ccxt==1.18.1021
|
||||
SQLAlchemy==1.3.6
|
||||
python-telegram-bot==11.1.0
|
||||
arrow==0.14.3
|
||||
arrow==0.14.4
|
||||
cachetools==3.1.1
|
||||
requests==2.22.0
|
||||
urllib3==1.24.2 # pyup: ignore
|
||||
urllib3==1.25.3
|
||||
wrapt==1.11.2
|
||||
scikit-learn==0.21.2
|
||||
scikit-learn==0.21.3
|
||||
joblib==0.13.2
|
||||
jsonschema==3.0.1
|
||||
jsonschema==3.0.2
|
||||
TA-Lib==0.4.17
|
||||
tabulate==0.8.3
|
||||
coinmarketcap==5.0.3
|
||||
@@ -20,7 +20,7 @@ scikit-optimize==0.5.2
|
||||
filelock==3.0.12
|
||||
|
||||
# find first, C search in arrays
|
||||
py_find_1st==1.1.3
|
||||
py_find_1st==1.1.4
|
||||
|
||||
#Load ticker files 30% faster
|
||||
python-rapidjson==0.7.2
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
-r requirements.txt
|
||||
-r requirements-plot.txt
|
||||
|
||||
coveralls==1.8.1
|
||||
coveralls==1.8.2
|
||||
flake8==3.7.8
|
||||
flake8-type-annotations==0.1.0
|
||||
flake8-tidy-imports==2.0.0
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
# Include all requirements to run the bot.
|
||||
-r requirements.txt
|
||||
|
||||
plotly==4.0.0
|
||||
plotly==4.1.0
|
||||
|
||||
|
||||
@@ -12,6 +12,7 @@ from freqtrade.configuration import Arguments, TimeRange
|
||||
from freqtrade.configuration import Configuration
|
||||
from freqtrade.configuration.arguments import ARGS_DOWNLOADER
|
||||
from freqtrade.configuration.check_exchange import check_exchange
|
||||
from freqtrade.configuration.load_config import load_config_file
|
||||
from freqtrade.data.history import download_pair_history
|
||||
from freqtrade.exchange import Exchange
|
||||
from freqtrade.misc import deep_merge_dicts
|
||||
@@ -40,7 +41,7 @@ if args.config:
|
||||
for path in args.config:
|
||||
logger.info(f"Using config: {path}...")
|
||||
# Merge config options, overwriting old values
|
||||
config = deep_merge_dicts(configuration._load_config_file(path), config)
|
||||
config = deep_merge_dicts(load_config_file(path), config)
|
||||
|
||||
config['stake_currency'] = ''
|
||||
# Ensure we do not use Exchange credentials
|
||||
|
||||
10
setup.py
10
setup.py
@@ -25,7 +25,13 @@ develop = [
|
||||
'pytest-random-order',
|
||||
]
|
||||
|
||||
all_extra = api + plot + develop
|
||||
jupyter = [
|
||||
'jupyter',
|
||||
'nbstripout',
|
||||
'ipykernel',
|
||||
]
|
||||
|
||||
all_extra = api + plot + develop + jupyter
|
||||
|
||||
setup(name='freqtrade',
|
||||
version=__version__,
|
||||
@@ -68,6 +74,8 @@ setup(name='freqtrade',
|
||||
'dev': all_extra,
|
||||
'plot': plot,
|
||||
'all': all_extra,
|
||||
'jupyter': jupyter,
|
||||
|
||||
},
|
||||
include_package_data=True,
|
||||
zip_safe=False,
|
||||
|
||||
39
setup.sh
39
setup.sh
@@ -11,6 +11,12 @@ function check_installed_pip() {
|
||||
|
||||
# Check which python version is installed
|
||||
function check_installed_python() {
|
||||
if [ -n "${VIRTUAL_ENV}" ]; then
|
||||
echo "Please deactivate your virtual environment before running setup.sh."
|
||||
echo "You can do this by running 'deactivate'."
|
||||
exit 2
|
||||
fi
|
||||
|
||||
which python3.7
|
||||
if [ $? -eq 0 ]; then
|
||||
echo "using Python 3.7"
|
||||
@@ -37,17 +43,19 @@ function updateenv() {
|
||||
echo "-------------------------"
|
||||
echo "Updating your virtual env"
|
||||
echo "-------------------------"
|
||||
if [ ! -f .env/bin/activate ]; then
|
||||
echo "Something went wrong, no virtual environment found."
|
||||
exit 1
|
||||
fi
|
||||
source .env/bin/activate
|
||||
echo "pip install in-progress. Please wait..."
|
||||
# Install numpy first to have py_find_1st install clean
|
||||
${PYTHON} -m pip install --upgrade pip numpy
|
||||
${PYTHON} -m pip install --upgrade -r requirements.txt
|
||||
|
||||
${PYTHON} -m pip install --upgrade pip
|
||||
read -p "Do you want to install dependencies for dev [y/N]? "
|
||||
if [[ $REPLY =~ ^[Yy]$ ]]
|
||||
then
|
||||
${PYTHON} -m pip install --upgrade -r requirements-dev.txt
|
||||
else
|
||||
${PYTHON} -m pip install --upgrade -r requirements.txt
|
||||
echo "Dev dependencies ignored."
|
||||
fi
|
||||
|
||||
@@ -70,6 +78,10 @@ function install_talib() {
|
||||
./configure --prefix=/usr/local
|
||||
make
|
||||
sudo make install
|
||||
if [ -x "$(command -v apt-get)" ]; then
|
||||
echo "Updating library path using ldconfig"
|
||||
sudo ldconfig
|
||||
fi
|
||||
cd .. && rm -rf ./ta-lib/
|
||||
cd ..
|
||||
}
|
||||
@@ -90,7 +102,7 @@ function install_macos() {
|
||||
# Install bot Debian_ubuntu
|
||||
function install_debian() {
|
||||
sudo apt-get update
|
||||
sudo apt-get install build-essential autoconf libtool pkg-config make wget git
|
||||
sudo apt-get install -y build-essential autoconf libtool pkg-config make wget git
|
||||
install_talib
|
||||
}
|
||||
|
||||
@@ -105,12 +117,12 @@ function reset() {
|
||||
echo "----------------------------"
|
||||
echo "Reseting branch and virtual env"
|
||||
echo "----------------------------"
|
||||
|
||||
if [ "1" == $(git branch -vv |grep -cE "\* develop|\* master") ]
|
||||
then
|
||||
if [ -d ".env" ]; then
|
||||
echo "- Delete your previous virtual env"
|
||||
rm -rf .env
|
||||
fi
|
||||
|
||||
read -p "Reset git branch? (This will remove all changes you made!) [y/N]? "
|
||||
if [[ $REPLY =~ ^[Yy]$ ]]; then
|
||||
|
||||
git fetch -a
|
||||
|
||||
@@ -123,12 +135,21 @@ function reset() {
|
||||
echo "- Hard resetting of 'master' branch."
|
||||
git reset --hard origin/master
|
||||
fi
|
||||
fi
|
||||
else
|
||||
echo "Reset ignored because you are not on 'master' or 'develop'."
|
||||
fi
|
||||
|
||||
if [ -d ".env" ]; then
|
||||
echo "- Delete your previous virtual env"
|
||||
rm -rf .env
|
||||
fi
|
||||
echo
|
||||
${PYTHON} -m venv .env
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Could not create virtual environment. Leaving now"
|
||||
exit 1
|
||||
fi
|
||||
updateenv
|
||||
}
|
||||
|
||||
|
||||
@@ -14,20 +14,27 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
from freqtrade.optimize.hyperopt_interface import IHyperOpt
|
||||
|
||||
|
||||
# This class is a sample. Feel free to customize it.
|
||||
class SampleHyperOpts(IHyperOpt):
|
||||
"""
|
||||
This is a test hyperopt to inspire you.
|
||||
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
|
||||
You can:
|
||||
- Rename the class name (Do not forget to update class_name)
|
||||
- Add any methods you want to build your hyperopt
|
||||
- Add any lib you need to build your hyperopt
|
||||
You must keep:
|
||||
- the prototype for the methods: populate_indicators, indicator_space, buy_strategy_generator,
|
||||
roi_space, generate_roi_table, stoploss_space
|
||||
"""
|
||||
This is a sample hyperopt to inspire you.
|
||||
Feel free to customize it.
|
||||
|
||||
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
|
||||
|
||||
You should:
|
||||
- Rename the class name to some unique name.
|
||||
- Add any methods you want to build your hyperopt.
|
||||
- Add any lib you need to build your hyperopt.
|
||||
|
||||
You must keep:
|
||||
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
|
||||
|
||||
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
|
||||
copied in every custom hyperopt. However, you may override them if you need the
|
||||
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
|
||||
Sample implementation of these methods can be found in
|
||||
https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
|
||||
"""
|
||||
@staticmethod
|
||||
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe['adx'] = ta.ADX(dataframe)
|
||||
@@ -167,42 +174,6 @@ class SampleHyperOpts(IHyperOpt):
|
||||
'sell-sar_reversal'], name='sell-trigger')
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def generate_roi_table(params: Dict) -> Dict[int, float]:
|
||||
"""
|
||||
Generate the ROI table that will be used by Hyperopt
|
||||
"""
|
||||
roi_table = {}
|
||||
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
|
||||
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
|
||||
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
|
||||
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
|
||||
|
||||
return roi_table
|
||||
|
||||
@staticmethod
|
||||
def stoploss_space() -> List[Dimension]:
|
||||
"""
|
||||
Stoploss Value to search
|
||||
"""
|
||||
return [
|
||||
Real(-0.5, -0.02, name='stoploss'),
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def roi_space() -> List[Dimension]:
|
||||
"""
|
||||
Values to search for each ROI steps
|
||||
"""
|
||||
return [
|
||||
Integer(10, 120, name='roi_t1'),
|
||||
Integer(10, 60, name='roi_t2'),
|
||||
Integer(10, 40, name='roi_t3'),
|
||||
Real(0.01, 0.04, name='roi_p1'),
|
||||
Real(0.01, 0.07, name='roi_p2'),
|
||||
Real(0.01, 0.20, name='roi_p3'),
|
||||
]
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators. Should be a copy of from strategy
|
||||
|
||||
261
user_data/hyperopts/sample_hyperopt_advanced.py
Normal file
261
user_data/hyperopts/sample_hyperopt_advanced.py
Normal file
@@ -0,0 +1,261 @@
|
||||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
|
||||
from functools import reduce
|
||||
from math import exp
|
||||
from typing import Any, Callable, Dict, List
|
||||
from datetime import datetime
|
||||
|
||||
import numpy as np# noqa F401
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
from skopt.space import Categorical, Dimension, Integer, Real
|
||||
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
from freqtrade.optimize.hyperopt_interface import IHyperOpt
|
||||
|
||||
|
||||
class AdvancedSampleHyperOpts(IHyperOpt):
|
||||
"""
|
||||
This is a sample hyperopt to inspire you.
|
||||
Feel free to customize it.
|
||||
|
||||
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
|
||||
|
||||
You should:
|
||||
- Rename the class name to some unique name.
|
||||
- Add any methods you want to build your hyperopt.
|
||||
- Add any lib you need to build your hyperopt.
|
||||
|
||||
You must keep:
|
||||
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
|
||||
|
||||
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
|
||||
copied in every custom hyperopt. However, you may override them if you need the
|
||||
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
|
||||
|
||||
This sample illustrates how to override these methods.
|
||||
"""
|
||||
@staticmethod
|
||||
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe['adx'] = ta.ADX(dataframe)
|
||||
macd = ta.MACD(dataframe)
|
||||
dataframe['macd'] = macd['macd']
|
||||
dataframe['macdsignal'] = macd['macdsignal']
|
||||
dataframe['mfi'] = ta.MFI(dataframe)
|
||||
dataframe['rsi'] = ta.RSI(dataframe)
|
||||
stoch_fast = ta.STOCHF(dataframe)
|
||||
dataframe['fastd'] = stoch_fast['fastd']
|
||||
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
||||
# Bollinger bands
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
||||
dataframe['bb_lowerband'] = bollinger['lower']
|
||||
dataframe['bb_upperband'] = bollinger['upper']
|
||||
dataframe['sar'] = ta.SAR(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
|
||||
"""
|
||||
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'])
|
||||
|
||||
# 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']
|
||||
))
|
||||
|
||||
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
|
||||
"""
|
||||
return [
|
||||
Integer(10, 25, name='mfi-value'),
|
||||
Integer(15, 45, name='fastd-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='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
|
||||
"""
|
||||
# print(params)
|
||||
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-rsi-enabled' in params and params['sell-rsi-enabled']:
|
||||
conditions.append(dataframe['rsi'] > params['sell-rsi-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']
|
||||
))
|
||||
|
||||
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-adx-value'),
|
||||
Integer(60, 100, name='sell-rsi-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-rsi-enabled'),
|
||||
Categorical(['sell-bb_upper',
|
||||
'sell-macd_cross_signal',
|
||||
'sell-sar_reversal'], name='sell-trigger')
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def generate_roi_table(params: Dict) -> Dict[int, float]:
|
||||
"""
|
||||
Generate the ROI table that will be used by Hyperopt
|
||||
|
||||
This implementation generates the default legacy Freqtrade ROI tables.
|
||||
|
||||
Change it if you need different number of steps in the generated
|
||||
ROI tables or other structure of the ROI tables.
|
||||
|
||||
Please keep it aligned with parameters in the 'roi' optimization
|
||||
hyperspace defined by the roi_space method.
|
||||
"""
|
||||
roi_table = {}
|
||||
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
|
||||
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
|
||||
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
|
||||
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
|
||||
|
||||
return roi_table
|
||||
|
||||
@staticmethod
|
||||
def roi_space() -> List[Dimension]:
|
||||
"""
|
||||
Values to search for each ROI steps
|
||||
|
||||
Override it if you need some different ranges for the parameters in the
|
||||
'roi' optimization hyperspace.
|
||||
|
||||
Please keep it aligned with the implementation of the
|
||||
generate_roi_table method.
|
||||
"""
|
||||
return [
|
||||
Integer(10, 120, name='roi_t1'),
|
||||
Integer(10, 60, name='roi_t2'),
|
||||
Integer(10, 40, name='roi_t3'),
|
||||
Real(0.01, 0.04, name='roi_p1'),
|
||||
Real(0.01, 0.07, name='roi_p2'),
|
||||
Real(0.01, 0.20, name='roi_p3'),
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def stoploss_space() -> List[Dimension]:
|
||||
"""
|
||||
Stoploss Value to search
|
||||
|
||||
Override it if you need some different range for the parameter in the
|
||||
'stoploss' optimization hyperspace.
|
||||
"""
|
||||
return [
|
||||
Real(-0.5, -0.02, name='stoploss'),
|
||||
]
|
||||
|
||||
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['close'] < dataframe['bb_lowerband']) &
|
||||
(dataframe['mfi'] < 16) &
|
||||
(dataframe['adx'] > 25) &
|
||||
(dataframe['rsi'] < 21)
|
||||
),
|
||||
'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['macdsignal'], dataframe['macd']
|
||||
)) &
|
||||
(dataframe['fastd'] > 54)
|
||||
),
|
||||
'sell'] = 1
|
||||
return dataframe
|
||||
0
user_data/notebooks/.gitkeep
Normal file
0
user_data/notebooks/.gitkeep
Normal file
243
user_data/notebooks/analysis_example.ipynb
Normal file
243
user_data/notebooks/analysis_example.ipynb
Normal file
@@ -0,0 +1,243 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Analyzing bot data\n",
|
||||
"\n",
|
||||
"You can analyze the results of backtests and trading history easily using Jupyter notebooks. \n",
|
||||
"**Copy this file so your changes don't get clobbered with the next freqtrade update!** \n",
|
||||
"For usage instructions, see [jupyter.org](https://jupyter.org/documentation). \n",
|
||||
"*Pro tip - Don't forget to start a jupyter notbook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Imports\n",
|
||||
"from pathlib import Path\n",
|
||||
"import os\n",
|
||||
"from freqtrade.data.history import load_pair_history\n",
|
||||
"from freqtrade.resolvers import StrategyResolver\n",
|
||||
"from freqtrade.data.btanalysis import load_backtest_data\n",
|
||||
"from freqtrade.data.btanalysis import load_trades_from_db"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Change directory\n",
|
||||
"# Define all paths relative to the project root shown in the cell output\n",
|
||||
"try:\n",
|
||||
" os.chdir(Path(Path.cwd(), '../..'))\n",
|
||||
" print(Path.cwd())\n",
|
||||
"except:\n",
|
||||
" pass"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Example snippets"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Load backtest results into a pandas dataframe"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load backtest results\n",
|
||||
"df = load_backtest_data(\"user_data/backtest_data/backtest-result.json\")\n",
|
||||
"\n",
|
||||
"# Show value-counts per pair\n",
|
||||
"df.groupby(\"pair\")[\"sell_reason\"].value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Load live trading results into a pandas dataframe"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Fetch trades from database\n",
|
||||
"df = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
|
||||
"\n",
|
||||
"# Display results\n",
|
||||
"df.groupby(\"pair\")[\"sell_reason\"].value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Strategy debugging example\n",
|
||||
"\n",
|
||||
"Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Import requirements and define variables used in analyses"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Define some constants\n",
|
||||
"ticker_interval = \"5m\"\n",
|
||||
"# Name of the strategy class\n",
|
||||
"strategy_name = 'AwesomeStrategy'\n",
|
||||
"# Path to user data\n",
|
||||
"user_data_dir = 'user_data'\n",
|
||||
"# Location of the strategy\n",
|
||||
"strategy_location = Path(user_data_dir, 'strategies')\n",
|
||||
"# Location of the data\n",
|
||||
"data_location = Path(user_data_dir, 'data', 'binance')\n",
|
||||
"# Pair to analyze \n",
|
||||
"# Only use one pair here\n",
|
||||
"pair = \"BTC_USDT\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Load exchange data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load data using values set above\n",
|
||||
"bt_data = load_pair_history(datadir=Path(data_location),\n",
|
||||
" ticker_interval=ticker_interval,\n",
|
||||
" pair=pair)\n",
|
||||
"\n",
|
||||
"# Confirm success\n",
|
||||
"print(\"Loaded \" + str(len(bt_data)) + f\" rows of data for {pair} from {data_location}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Load and run strategy\n",
|
||||
"* Rerun each time the strategy file is changed"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load strategy using values set above\n",
|
||||
"strategy = StrategyResolver({'strategy': strategy_name,\n",
|
||||
" 'user_data_dir': user_data_dir,\n",
|
||||
" 'strategy_path': strategy_location}).strategy\n",
|
||||
"\n",
|
||||
"# Generate buy/sell signals using strategy\n",
|
||||
"df = strategy.analyze_ticker(bt_data, {'pair': pair})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Display the trade details\n",
|
||||
"* Note that using `data.head()` would also work, however most indicators have some \"startup\" data at the top of the dataframe.\n",
|
||||
"\n",
|
||||
"#### Some possible problems\n",
|
||||
"\n",
|
||||
"* Columns with NaN values at the end of the dataframe\n",
|
||||
"* Columns used in `crossed*()` functions with completely different units\n",
|
||||
"\n",
|
||||
"#### Comparison with full backtest\n",
|
||||
"\n",
|
||||
"having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.\n",
|
||||
"\n",
|
||||
"Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple \"buy\" signals for each pair in sequence (until rsi returns > 29).\n",
|
||||
"The bot will only buy on the first of these signals (and also only if a trade-slot (\"max_open_trades\") is still available), or on one of the middle signals, as soon as a \"slot\" becomes available.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Report results\n",
|
||||
"print(f\"Generated {df['buy'].sum()} buy signals\")\n",
|
||||
"data = df.set_index('date', drop=True)\n",
|
||||
"data.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"file_extension": ".py",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.3"
|
||||
},
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"npconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": 3
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in New Issue
Block a user