Revert "Merge branch 'plot_hyperopt_stats' into opt-ask-force-new-points"

This reverts commit 4eb9cc6e8b, reversing
changes made to a3b401a762.
This commit is contained in:
Italo 2022-03-30 09:39:07 +01:00
parent 4eb9cc6e8b
commit 9f171193ef
15 changed files with 169 additions and 184 deletions

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@ -31,14 +31,14 @@ jobs:
python-version: ${{ matrix.python-version }} python-version: ${{ matrix.python-version }}
- name: Cache_dependencies - name: Cache_dependencies
uses: actions/cache@v3 uses: actions/cache@v2
id: cache id: cache
with: with:
path: ~/dependencies/ path: ~/dependencies/
key: ${{ runner.os }}-dependencies key: ${{ runner.os }}-dependencies
- name: pip cache (linux) - name: pip cache (linux)
uses: actions/cache@v3 uses: actions/cache@v2
if: runner.os == 'Linux' if: runner.os == 'Linux'
with: with:
path: ~/.cache/pip path: ~/.cache/pip
@ -126,14 +126,14 @@ jobs:
python-version: ${{ matrix.python-version }} python-version: ${{ matrix.python-version }}
- name: Cache_dependencies - name: Cache_dependencies
uses: actions/cache@v3 uses: actions/cache@v2
id: cache id: cache
with: with:
path: ~/dependencies/ path: ~/dependencies/
key: ${{ runner.os }}-dependencies key: ${{ runner.os }}-dependencies
- name: pip cache (macOS) - name: pip cache (macOS)
uses: actions/cache@v3 uses: actions/cache@v2
if: runner.os == 'macOS' if: runner.os == 'macOS'
with: with:
path: ~/Library/Caches/pip path: ~/Library/Caches/pip
@ -218,7 +218,7 @@ jobs:
python-version: ${{ matrix.python-version }} python-version: ${{ matrix.python-version }}
- name: Pip cache (Windows) - name: Pip cache (Windows)
uses: actions/cache@v3 uses: actions/cache@preview
with: with:
path: ~\AppData\Local\pip\Cache path: ~\AppData\Local\pip\Cache
key: ${{ matrix.os }}-${{ matrix.python-version }}-pip key: ${{ matrix.os }}-${{ matrix.python-version }}-pip

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@ -10,7 +10,7 @@ jobs:
steps: steps:
- uses: actions/checkout@v3 - uses: actions/checkout@v3
- name: Docker Hub Description - name: Docker Hub Description
uses: peter-evans/dockerhub-description@v3 uses: peter-evans/dockerhub-description@v2.4.3
env: env:
DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }} DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }}
DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }} DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}

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@ -51,9 +51,9 @@ When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Fre
#### Buy price without Orderbook enabled #### Buy price without Orderbook enabled
The following section uses `side` as the configured `bid_strategy.price_side` (defaults to `"bid"`). The following section uses `side` as the configured `bid_strategy.price_side`.
When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price based on `bid_strategy.ask_last_balance`.. When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price. The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
@ -88,9 +88,9 @@ When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Fr
#### Sell price without Orderbook enabled #### Sell price without Orderbook enabled
The following section uses `side` as the configured `ask_strategy.price_side` (defaults to `"ask"`). When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
When not using orderbook (`ask_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's above the `last` traded price from the ticker. Otherwise (when the `side` price is below the `last` price), it calculates a rate between `side` and `last` price based on `ask_strategy.bid_last_balance`. When not using orderbook (`ask_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
The `ask_strategy.bid_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the last price and values between those interpolate between `side` and last price. The `ask_strategy.bid_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the last price and values between those interpolate between `side` and last price.

View File

@ -1,5 +1,4 @@
mkdocs==1.3.0 mkdocs==1.2.3
mkdocs-material==8.2.8 mkdocs-material==8.2.5
mdx_truly_sane_lists==1.2 mdx_truly_sane_lists==1.2
pymdown-extensions==9.3 pymdown-extensions==9.2
jinja2==3.1.1

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@ -146,7 +146,7 @@ def version(self) -> str:
The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched: The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
``` python title="user_data/strategies/myawesomestrategy.py" ``` python
class MyAwesomeStrategy(IStrategy): class MyAwesomeStrategy(IStrategy):
... ...
stoploss = 0.13 stoploss = 0.13
@ -155,10 +155,6 @@ class MyAwesomeStrategy(IStrategy):
# should be in any custom strategy... # should be in any custom strategy...
... ...
```
``` python title="user_data/strategies/MyAwesomeStrategy2.py"
from myawesomestrategy import MyAwesomeStrategy
class MyAwesomeStrategy2(MyAwesomeStrategy): class MyAwesomeStrategy2(MyAwesomeStrategy):
# Override something # Override something
stoploss = 0.08 stoploss = 0.08
@ -167,7 +163,16 @@ class MyAwesomeStrategy2(MyAwesomeStrategy):
Both attributes and methods may be overridden, altering behavior of the original strategy in a way you need. Both attributes and methods may be overridden, altering behavior of the original strategy in a way you need.
While keeping the subclass in the same file is technically possible, it can lead to some problems with hyperopt parameter files, we therefore recommend to use separate strategy files, and import the parent strategy as shown above. !!! Note "Parent-strategy in different files"
If you have the parent-strategy in a different file, you'll need to add the following to the top of your "child"-file to ensure proper loading, otherwise freqtrade may not be able to load the parent strategy correctly.
``` python
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent))
from myawesomestrategy import MyAwesomeStrategy
```
## Embedding Strategies ## Embedding Strategies

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@ -1,14 +1,27 @@
""" Freqtrade bot """ """ Freqtrade bot """
__version__ = 'develop' __version__ = 'develop'
if 'dev' in __version__: if __version__ == 'develop':
try: try:
import subprocess import subprocess
__version__ = __version__ + '-' + subprocess.check_output( __version__ = 'develop-' + subprocess.check_output(
['git', 'log', '--format="%h"', '-n 1'], ['git', 'log', '--format="%h"', '-n 1'],
stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"') stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
# from datetime import datetime
# last_release = subprocess.check_output(
# ['git', 'tag']
# ).decode('utf-8').split()[-1].split(".")
# # Releases are in the format "2020.1" - we increment the latest version for dev.
# prefix = f"{last_release[0]}.{int(last_release[1]) + 1}"
# dev_version = int(datetime.now().timestamp() // 1000)
# __version__ = f"{prefix}.dev{dev_version}"
# subprocess.check_output(
# ['git', 'log', '--format="%h"', '-n 1'],
# stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
except Exception: # pragma: no cover except Exception: # pragma: no cover
# git not available, ignore # git not available, ignore
try: try:

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@ -32,24 +32,20 @@ from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F4
from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
from freqtrade.optimize.optimize_reports import generate_strategy_stats from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
import matplotlib.pyplot as plt
import numpy as np
import random
# Suppress scikit-learn FutureWarnings from skopt # Suppress scikit-learn FutureWarnings from skopt
with warnings.catch_warnings(): with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning) warnings.filterwarnings("ignore", category=FutureWarning)
from skopt import Optimizer from skopt import Optimizer
from skopt.space import Dimension from skopt.space import Dimension
from sklearn.model_selection import cross_val_score
from skopt.plots import plot_convergence, plot_regret, plot_evaluations, plot_objective
progressbar.streams.wrap_stderr() progressbar.streams.wrap_stderr()
progressbar.streams.wrap_stdout() progressbar.streams.wrap_stdout()
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
INITIAL_POINTS = 32 INITIAL_POINTS = 30
# Keep no more than SKOPT_MODEL_QUEUE_SIZE models # Keep no more than SKOPT_MODEL_QUEUE_SIZE models
# in the skopt model queue, to optimize memory consumption # in the skopt model queue, to optimize memory consumption
@ -414,35 +410,6 @@ class Hyperopt:
# Store non-trimmed data - will be trimmed after signal generation. # Store non-trimmed data - will be trimmed after signal generation.
dump(preprocessed, self.data_pickle_file) dump(preprocessed, self.data_pickle_file)
def get_asked_points(self, n_points: int) -> List[List[Any]]:
'''
Enforce points returned from `self.opt.ask` have not been already evaluated
Steps:
1. Try to get points using `self.opt.ask` first
2. Discard the points that have already been evaluated
3. Retry using `self.opt.ask` up to 3 times
4. If still some points are missing in respect to `n_points`, random sample some points
5. Repeat until at least `n_points` points in the `asked_non_tried` list
6. Return a list with length truncated at `n_points`
'''
i = 0
asked_non_tried: List[List[Any]] = []
while i < 100 and len(asked_non_tried) < n_points:
if i < 3:
self.opt.cache_ = {}
asked = self.opt.ask(n_points=n_points * 5)
else:
asked = self.opt.space.rvs(n_samples=n_points * 5)
asked_non_tried += [x for x in asked
if x not in self.opt.Xi
and x not in asked_non_tried]
i += 1
if asked_non_tried:
return asked_non_tried[:min(len(asked_non_tried), n_points)]
else:
return self.opt.ask(n_points=n_points)
def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]: def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]:
''' '''
Enforce points returned from `self.opt.ask` have not been already evaluated Enforce points returned from `self.opt.ask` have not been already evaluated
@ -548,13 +515,7 @@ class Hyperopt:
asked, is_random = self.get_asked_points(n_points=current_jobs) asked, is_random = self.get_asked_points(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i) f_val = self.run_optimizer_parallel(parallel, asked, i)
res = self.opt.tell(asked, [v['loss'] for v in f_val]) self.opt.tell(asked, [v['loss'] for v in f_val])
self.plot_optimizer(res, path='user_data/scripts', convergence=False, regret=False, r2=False, objective=True, jobs=jobs)
if res.models and hasattr(res.models[-1], "kernel_"):
print(f'kernel: {res.models[-1].kernel_}')
print(datetime.now())
# Calculate progressbar outputs # Calculate progressbar outputs
for j, val in enumerate(f_val): for j, val in enumerate(f_val):
@ -600,47 +561,3 @@ class Hyperopt:
# This is printed when Ctrl+C is pressed quickly, before first epochs have # This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated. # a chance to be evaluated.
print("No epochs evaluated yet, no best result.") print("No epochs evaluated yet, no best result.")
def plot_r2(self, res, ax, jobs):
if len(res.x_iters) < 10:
return
if not hasattr(self, 'r2_list'):
self.r2_list = []
model = res.models[-1]
model.criterion = 'squared_error'
r2 = cross_val_score(model, X=res.x_iters, y=res.func_vals, scoring='r2', cv=5, n_jobs=jobs).mean()
r2 = r2 if r2 > -5 else -5
self.r2_list.append(r2)
ax.plot(range(INITIAL_POINTS, INITIAL_POINTS + jobs * len(self.r2_list), jobs), self.r2_list, label='R2', marker=".", markersize=12, lw=2)
def plot_optimizer(self, res, path, jobs, convergence=True, regret=True, evaluations=True, objective=True, r2=True):
path = Path(path)
if convergence:
ax = plot_convergence(res)
ax.flatten()[0].figure.savefig(path / 'convergence.png')
if regret:
ax = plot_regret(res)
ax.flatten()[0].figure.savefig(path / 'regret.png')
if evaluations:
# print('evaluations')
ax = plot_evaluations(res)
ax.flatten()[0].figure.savefig(path / 'evaluations.png')
if objective and res.models:
# print('objective')
ax = plot_objective(res, sample_source='result', n_samples=50, n_points=10)
ax.flatten()[0].figure.savefig(path / 'objective.png')
if r2 and res.models:
fig, ax = plt.subplots()
ax.set_ylabel('R2')
ax.set_xlabel('Epoch')
ax.set_title('R2')
ax = self.plot_r2(res, ax, jobs)
fig.savefig(path / 'r2.png')

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@ -6,7 +6,6 @@ This module load custom objects
import importlib.util import importlib.util
import inspect import inspect
import logging import logging
import sys
from pathlib import Path from pathlib import Path
from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union
@ -16,22 +15,6 @@ from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
class PathModifier:
def __init__(self, path: Path):
self.path = path
def __enter__(self):
"""Inject path to allow importing with relative imports."""
sys.path.insert(0, str(self.path))
return self
def __exit__(self, exc_type, exc_val, exc_tb):
"""Undo insertion of local path."""
str_path = str(self.path)
if str_path in sys.path:
sys.path.remove(str_path)
class IResolver: class IResolver:
""" """
This class contains all the logic to load custom classes This class contains all the logic to load custom classes
@ -74,9 +57,7 @@ class IResolver:
# Generate spec based on absolute path # Generate spec based on absolute path
# Pass object_name as first argument to have logging print a reasonable name. # Pass object_name as first argument to have logging print a reasonable name.
with PathModifier(module_path.parent): spec = importlib.util.spec_from_file_location(object_name or "", str(module_path))
module_name = module_path.stem or ""
spec = importlib.util.spec_from_file_location(module_name, str(module_path))
if not spec: if not spec:
return iter([None]) return iter([None])
@ -94,11 +75,8 @@ class IResolver:
name, obj in inspect.getmembers( name, obj in inspect.getmembers(
module, inspect.isclass) if ((object_name is None or object_name == name) module, inspect.isclass) if ((object_name is None or object_name == name)
and issubclass(obj, cls.object_type) and issubclass(obj, cls.object_type)
and obj is not cls.object_type and obj is not cls.object_type)
and obj.__module__ == module_name
) )
)
# The __module__ check ensures we only use strategies that are defined in this folder.
return valid_objects_gen return valid_objects_gen
@classmethod @classmethod

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@ -6,9 +6,9 @@
coveralls==3.3.1 coveralls==3.3.1
flake8==4.0.1 flake8==4.0.1
flake8-tidy-imports==4.6.0 flake8-tidy-imports==4.6.0
mypy==0.942 mypy==0.940
pytest==7.1.1 pytest==7.1.0
pytest-asyncio==0.18.3 pytest-asyncio==0.18.2
pytest-cov==3.0.0 pytest-cov==3.0.0
pytest-mock==3.7.0 pytest-mock==3.7.0
pytest-random-order==1.0.4 pytest-random-order==1.0.4
@ -22,8 +22,8 @@ nbconvert==6.4.4
# mypy types # mypy types
types-cachetools==5.0.0 types-cachetools==5.0.0
types-filelock==3.2.5 types-filelock==3.2.5
types-requests==2.27.15 types-requests==2.27.12
types-tabulate==0.8.6 types-tabulate==0.8.5
# Extensions to datetime library # Extensions to datetime library
types-python-dateutil==2.8.10 types-python-dateutil==2.8.9

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@ -8,4 +8,3 @@ scikit-optimize==0.9.0
filelock==3.6.0 filelock==3.6.0
joblib==1.1.0 joblib==1.1.0
progressbar2==4.0.0 progressbar2==4.0.0
matplotlib

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@ -2,22 +2,22 @@ numpy==1.22.3
pandas==1.4.1 pandas==1.4.1
pandas-ta==0.3.14b pandas-ta==0.3.14b
ccxt==1.77.36 ccxt==1.76.5
# Pin cryptography for now due to rust build errors with piwheels # Pin cryptography for now due to rust build errors with piwheels
cryptography==36.0.2 cryptography==36.0.1
aiohttp==3.8.1 aiohttp==3.8.1
SQLAlchemy==1.4.32 SQLAlchemy==1.4.32
python-telegram-bot==13.11 python-telegram-bot==13.11
arrow==1.2.2 arrow==1.2.2
cachetools==4.2.2 cachetools==4.2.2
requests==2.27.1 requests==2.27.1
urllib3==1.26.9 urllib3==1.26.8
jsonschema==4.4.0 jsonschema==4.4.0
TA-Lib==0.4.24 TA-Lib==0.4.24
technical==1.3.0 technical==1.3.0
tabulate==0.8.9 tabulate==0.8.9
pycoingecko==2.2.0 pycoingecko==2.2.0
jinja2==3.1.1 jinja2==3.0.3
tables==3.7.0 tables==3.7.0
blosc==1.10.6 blosc==1.10.6

View File

@ -1019,8 +1019,8 @@ def limit_buy_order_open():
'type': 'limit', 'type': 'limit',
'side': 'buy', 'side': 'buy',
'symbol': 'mocked', 'symbol': 'mocked',
'timestamp': arrow.utcnow().int_timestamp * 1000,
'datetime': arrow.utcnow().isoformat(), 'datetime': arrow.utcnow().isoformat(),
'timestamp': arrow.utcnow().int_timestamp,
'price': 0.00001099, 'price': 0.00001099,
'amount': 90.99181073, 'amount': 90.99181073,
'filled': 0.0, 'filled': 0.0,
@ -1046,7 +1046,6 @@ def market_buy_order():
'type': 'market', 'type': 'market',
'side': 'buy', 'side': 'buy',
'symbol': 'mocked', 'symbol': 'mocked',
'timestamp': arrow.utcnow().int_timestamp * 1000,
'datetime': arrow.utcnow().isoformat(), 'datetime': arrow.utcnow().isoformat(),
'price': 0.00004099, 'price': 0.00004099,
'amount': 91.99181073, 'amount': 91.99181073,
@ -1063,7 +1062,6 @@ def market_sell_order():
'type': 'market', 'type': 'market',
'side': 'sell', 'side': 'sell',
'symbol': 'mocked', 'symbol': 'mocked',
'timestamp': arrow.utcnow().int_timestamp * 1000,
'datetime': arrow.utcnow().isoformat(), 'datetime': arrow.utcnow().isoformat(),
'price': 0.00004173, 'price': 0.00004173,
'amount': 91.99181073, 'amount': 91.99181073,
@ -1080,8 +1078,7 @@ def limit_buy_order_old():
'type': 'limit', 'type': 'limit',
'side': 'buy', 'side': 'buy',
'symbol': 'mocked', 'symbol': 'mocked',
'datetime': arrow.utcnow().shift(minutes=-601).isoformat(), 'datetime': str(arrow.utcnow().shift(minutes=-601).datetime),
'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000,
'price': 0.00001099, 'price': 0.00001099,
'amount': 90.99181073, 'amount': 90.99181073,
'filled': 0.0, 'filled': 0.0,
@ -1097,7 +1094,6 @@ def limit_sell_order_old():
'type': 'limit', 'type': 'limit',
'side': 'sell', 'side': 'sell',
'symbol': 'ETH/BTC', 'symbol': 'ETH/BTC',
'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000,
'datetime': arrow.utcnow().shift(minutes=-601).isoformat(), 'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
'price': 0.00001099, 'price': 0.00001099,
'amount': 90.99181073, 'amount': 90.99181073,
@ -1114,7 +1110,6 @@ def limit_buy_order_old_partial():
'type': 'limit', 'type': 'limit',
'side': 'buy', 'side': 'buy',
'symbol': 'ETH/BTC', 'symbol': 'ETH/BTC',
'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000,
'datetime': arrow.utcnow().shift(minutes=-601).isoformat(), 'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
'price': 0.00001099, 'price': 0.00001099,
'amount': 90.99181073, 'amount': 90.99181073,
@ -1144,7 +1139,7 @@ def limit_buy_order_canceled_empty(request):
'info': {}, 'info': {},
'id': '1234512345', 'id': '1234512345',
'clientOrderId': None, 'clientOrderId': None,
'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000, 'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp,
'datetime': arrow.utcnow().shift(minutes=-601).isoformat(), 'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
'lastTradeTimestamp': None, 'lastTradeTimestamp': None,
'symbol': 'LTC/USDT', 'symbol': 'LTC/USDT',
@ -1165,7 +1160,7 @@ def limit_buy_order_canceled_empty(request):
'info': {}, 'info': {},
'id': 'AZNPFF-4AC4N-7MKTAT', 'id': 'AZNPFF-4AC4N-7MKTAT',
'clientOrderId': None, 'clientOrderId': None,
'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000, 'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp,
'datetime': arrow.utcnow().shift(minutes=-601).isoformat(), 'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
'lastTradeTimestamp': None, 'lastTradeTimestamp': None,
'status': 'canceled', 'status': 'canceled',
@ -1186,7 +1181,7 @@ def limit_buy_order_canceled_empty(request):
'info': {}, 'info': {},
'id': '1234512345', 'id': '1234512345',
'clientOrderId': 'alb1234123', 'clientOrderId': 'alb1234123',
'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000, 'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp,
'datetime': arrow.utcnow().shift(minutes=-601).isoformat(), 'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
'lastTradeTimestamp': None, 'lastTradeTimestamp': None,
'symbol': 'LTC/USDT', 'symbol': 'LTC/USDT',
@ -1207,7 +1202,7 @@ def limit_buy_order_canceled_empty(request):
'info': {}, 'info': {},
'id': '1234512345', 'id': '1234512345',
'clientOrderId': 'alb1234123', 'clientOrderId': 'alb1234123',
'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000, 'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp,
'datetime': arrow.utcnow().shift(minutes=-601).isoformat(), 'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
'lastTradeTimestamp': None, 'lastTradeTimestamp': None,
'symbol': 'LTC/USDT', 'symbol': 'LTC/USDT',
@ -1233,7 +1228,7 @@ def limit_sell_order_open():
'side': 'sell', 'side': 'sell',
'symbol': 'mocked', 'symbol': 'mocked',
'datetime': arrow.utcnow().isoformat(), 'datetime': arrow.utcnow().isoformat(),
'timestamp': arrow.utcnow().int_timestamp * 1000, 'timestamp': arrow.utcnow().int_timestamp,
'price': 0.00001173, 'price': 0.00001173,
'amount': 90.99181073, 'amount': 90.99181073,
'filled': 0.0, 'filled': 0.0,
@ -1399,7 +1394,7 @@ def tickers():
'BLK/BTC': { 'BLK/BTC': {
'symbol': 'BLK/BTC', 'symbol': 'BLK/BTC',
'timestamp': 1522014806072, 'timestamp': 1522014806072,
'datetime': '2018-03-25T21:53:26.072Z', 'datetime': '2018-03-25T21:53:26.720Z',
'high': 0.007745, 'high': 0.007745,
'low': 0.007512, 'low': 0.007512,
'bid': 0.007729, 'bid': 0.007729,
@ -1895,8 +1890,7 @@ def buy_order_fee():
'type': 'limit', 'type': 'limit',
'side': 'buy', 'side': 'buy',
'symbol': 'mocked', 'symbol': 'mocked',
'timestamp': arrow.utcnow().shift(minutes=-601).int_timestamp * 1000, 'datetime': str(arrow.utcnow().shift(minutes=-601).datetime),
'datetime': arrow.utcnow().shift(minutes=-601).isoformat(),
'price': 0.245441, 'price': 0.245441,
'amount': 8.0, 'amount': 8.0,
'cost': 1.963528, 'cost': 1.963528,
@ -2205,7 +2199,7 @@ def limit_buy_order_usdt_open():
'side': 'buy', 'side': 'buy',
'symbol': 'mocked', 'symbol': 'mocked',
'datetime': arrow.utcnow().isoformat(), 'datetime': arrow.utcnow().isoformat(),
'timestamp': arrow.utcnow().int_timestamp * 1000, 'timestamp': arrow.utcnow().int_timestamp,
'price': 2.00, 'price': 2.00,
'amount': 30.0, 'amount': 30.0,
'filled': 0.0, 'filled': 0.0,
@ -2232,7 +2226,7 @@ def limit_sell_order_usdt_open():
'side': 'sell', 'side': 'sell',
'symbol': 'mocked', 'symbol': 'mocked',
'datetime': arrow.utcnow().isoformat(), 'datetime': arrow.utcnow().isoformat(),
'timestamp': arrow.utcnow().int_timestamp * 1000, 'timestamp': arrow.utcnow().int_timestamp,
'price': 2.20, 'price': 2.20,
'amount': 30.0, 'amount': 30.0,
'filled': 0.0, 'filled': 0.0,
@ -2257,7 +2251,6 @@ def market_buy_order_usdt():
'type': 'market', 'type': 'market',
'side': 'buy', 'side': 'buy',
'symbol': 'mocked', 'symbol': 'mocked',
'timestamp': arrow.utcnow().int_timestamp * 1000,
'datetime': arrow.utcnow().isoformat(), 'datetime': arrow.utcnow().isoformat(),
'price': 2.00, 'price': 2.00,
'amount': 30.0, 'amount': 30.0,
@ -2314,7 +2307,6 @@ def market_sell_order_usdt():
'type': 'market', 'type': 'market',
'side': 'sell', 'side': 'sell',
'symbol': 'mocked', 'symbol': 'mocked',
'timestamp': arrow.utcnow().int_timestamp * 1000,
'datetime': arrow.utcnow().isoformat(), 'datetime': arrow.utcnow().isoformat(),
'price': 2.20, 'price': 2.20,
'amount': 30.0, 'amount': 30.0,

View File

@ -1098,7 +1098,7 @@ def test_create_order(default_conf, mocker, side, ordertype, rate, marketprice,
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name) exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
order = exchange.create_order( order = exchange.create_order(
pair='ETH/BTC', ordertype=ordertype, side=side, amount=1, rate=rate) pair='ETH/BTC', ordertype=ordertype, side=side, amount=1, rate=200)
assert 'id' in order assert 'id' in order
assert 'info' in order assert 'info' in order

View File

@ -1,13 +1,14 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import talib.abstract as ta
from pandas import DataFrame from pandas import DataFrame
from strategy_test_v2 import StrategyTestV2
import freqtrade.vendor.qtpylib.indicators as qtpylib import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import BooleanParameter, DecimalParameter, IntParameter, RealParameter from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,
RealParameter)
class HyperoptableStrategy(StrategyTestV2): class HyperoptableStrategy(IStrategy):
""" """
Default Strategy provided by freqtrade bot. Default Strategy provided by freqtrade bot.
Please do not modify this strategy, it's intended for internal use only. Please do not modify this strategy, it's intended for internal use only.
@ -15,6 +16,38 @@ class HyperoptableStrategy(StrategyTestV2):
or strategy repository https://github.com/freqtrade/freqtrade-strategies or strategy repository https://github.com/freqtrade/freqtrade-strategies
for samples and inspiration. for samples and inspiration.
""" """
INTERFACE_VERSION = 2
# Minimal ROI designed for the strategy
minimal_roi = {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy
stoploss = -0.10
# Optimal ticker interval for the strategy
timeframe = '5m'
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': False
}
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 20
# Optional time in force for orders
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc',
}
buy_params = { buy_params = {
'buy_rsi': 35, 'buy_rsi': 35,
@ -58,6 +91,55 @@ class HyperoptableStrategy(StrategyTestV2):
""" """
return [] return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# Momentum Indicator
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# Minus Directional Indicator / Movement
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# EMA - Exponential Moving Average
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
""" """
Based on TA indicators, populates the buy signal for the given dataframe Based on TA indicators, populates the buy signal for the given dataframe

View File

@ -7,7 +7,7 @@ from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy from freqtrade.strategy.interface import IStrategy
class StrategyTestV2(IStrategy): class StrategyTestV2(IStrategy):