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Merge branch 'develop' into align_userdata
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Matthias
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1
.github/ISSUE_TEMPLATE.md
vendored
1
.github/ISSUE_TEMPLATE.md
vendored
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@ -5,6 +5,7 @@ If it hasn't been reported, please create a new issue.
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|||
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## Step 2: Describe your environment
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* Operating system: ____
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* Python Version: _____ (`python -V`)
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* CCXT version: _____ (`pip freeze | grep ccxt`)
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* Branch: Master | Develop
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|
|
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@ -6,6 +6,128 @@ A good way for this is using Jupyter (notebook or lab) - which provides an inter
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The following helpers will help you loading the data into Pandas DataFrames, and may also give you some starting points in analyzing the results.
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## Strategy development problem analysis
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Debugging a strategy (are there no buy signals, ...) can be very time-consuming.
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FreqTrade tries to help you by exposing a few helper-functions, which can be very handy.
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It's recommended using Juptyer Notebooks for analysis, since it offers a dynamic way to rerun certain parts of the code.
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The following is a full code-snippet, which will be explained by both comments, and step by step below.
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|
||||
```python
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# Some necessary imports
|
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from pathlib import Path
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from freqtrade.data.history import load_pair_history
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from freqtrade.resolvers import StrategyResolver
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# Define some constants
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ticker_interval = "5m"
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# Name of the strategy class
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strategyname = 'Awesomestrategy'
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# Location of the strategy
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strategy_location = '../xmatt/strategies'
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# Location of the data
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data_location = '../freqtrade/user_data/data/binance/'
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# Only use one pair here
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pair = "XRP_ETH"
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### End constants
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# Load data
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bt_data = load_pair_history(datadir=Path(data_location),
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ticker_interval = ticker_interval,
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pair=pair)
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print(len(bt_data))
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### Start strategy reload
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# Load strategy - best done in a new cell
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# Rerun each time the strategy-file is changed.
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strategy = StrategyResolver({'strategy': strategyname,
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'user_data_dir': Path.cwd(),
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'strategy_path': location}).strategy
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# Run strategy (just like in backtesting)
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df = strategy.analyze_ticker(bt_data, {'pair': pair})
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print(f"Generated {df['buy'].sum()} buy signals")
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# Reindex data to be "nicer" and show data
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data = df.set_index('date', drop=True)
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data.tail()
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```
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### Explanation
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|
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#### Imports and constant definition
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||||
|
||||
``` python
|
||||
# Some necessary imports
|
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from pathlib import Path
|
||||
|
||||
from freqtrade.data.history import load_pair_history
|
||||
from freqtrade.resolvers import StrategyResolver
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# Define some constants
|
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ticker_interval = "5m"
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|
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# Name of the strategy class
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strategyname = 'Awesomestrategy'
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# Location of the strategy
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strategy_location = 'user_data/strategies'
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# Location of the data
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data_location = 'user_data/data/binance'
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# Only use one pair here
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pair = "XRP_ETH"
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```
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|
||||
This first section imports necessary modules, and defines some constants you'll probably need to adjust for your case.
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||||
#### Load candles
|
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|
||||
``` python
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# Load data
|
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bt_data = load_pair_history(datadir=Path(data_location),
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ticker_interval = ticker_interval,
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pair=pair)
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print(len(bt_data))
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```
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|
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This second section loads the historic data and prints the amount of candles in the DataFrame.
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You can also inspect this dataframe by using `bt_data.head()` or `bt_data.tail()`.
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|
||||
#### 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.
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||||
|
||||
``` python
|
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# Load strategy - best done in a new cell
|
||||
# Needs to be ran each time the strategy-file is changed.
|
||||
strategy = StrategyResolver({'strategy': strategyname,
|
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'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.
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|
||||
The last 2 lines serve to analyze the dataframe in detail.
|
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This can be important if your strategy did not generate any buy signals.
|
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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.
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|
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There can be many things wrong, some signs to look for are:
|
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|
||||
* Columns with NaN values at the end of the dataframe
|
||||
* Columns used in `crossed*()` functions with completely different units
|
||||
|
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## Backtesting
|
||||
|
||||
To analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
|
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|
|
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10
docs/faq.md
10
docs/faq.md
|
|
@ -45,6 +45,16 @@ the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-c
|
|||
|
||||
You can use the `/forcesell all` command from Telegram.
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|
||||
### I get the message "RESTRICTED_MARKET"
|
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|
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Currently known to happen for US Bittrex users.
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Bittrex split its exchange into US and International versions.
|
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The International version has more pairs available, however the API always returns all pairs, so there is currently no automated way to detect if you're affected by the restriction.
|
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|
||||
If you have restricted pairs in your whitelist, you'll get a warning message in the log on FreqTrade startup for each restricted pair.
|
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If you're an "International" Customer on the Bittrex exchange, then this warning will probably not impact you.
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If you're a US customer, the bot will fail to create orders for these pairs, and you should remove them from your Whitelist.
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|
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## Hyperopt module
|
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|
||||
### How many epoch do I need to get a good Hyperopt result?
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||||
|
|
|
|||
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|
@ -303,8 +303,10 @@ Given the following result from hyperopt:
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|||
|
||||
```
|
||||
Best result:
|
||||
135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
|
||||
with values:
|
||||
|
||||
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,
|
||||
|
|
@ -347,21 +349,15 @@ If you are optimizing ROI, you're result will look as follows and include a ROI
|
|||
|
||||
```
|
||||
Best result:
|
||||
135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
|
||||
with values:
|
||||
|
||||
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,
|
||||
'adx-enabled': False,
|
||||
'rsi-enabled': True,
|
||||
'trigger': 'bb_lower',
|
||||
'roi_t1': 40,
|
||||
'roi_t2': 57,
|
||||
'roi_t3': 21,
|
||||
'roi_p1': 0.03634636907306948,
|
||||
'roi_p2': 0.055237357937802885,
|
||||
'roi_p3': 0.015163796015548354,
|
||||
'stoploss': -0.37996664668703606
|
||||
}
|
||||
'trigger': 'bb_lower'}
|
||||
ROI table:
|
||||
{ 0: 0.10674752302642071,
|
||||
21: 0.09158372701087236,
|
||||
|
|
@ -372,9 +368,9 @@ ROI table:
|
|||
This would translate to the following ROI table:
|
||||
|
||||
``` python
|
||||
minimal_roi = {
|
||||
minimal_roi = {
|
||||
"118": 0,
|
||||
"78": 0.0363463,
|
||||
"78": 0.0363,
|
||||
"21": 0.0915,
|
||||
"0": 0.106
|
||||
}
|
||||
|
|
|
|||
|
|
@ -81,19 +81,30 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
|
|||
"""
|
||||
trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
|
||||
persistence.init(db_url, clean_open_orders=False)
|
||||
columns = ["pair", "profit", "open_time", "close_time",
|
||||
"open_rate", "close_rate", "duration", "sell_reason",
|
||||
"max_rate", "min_rate"]
|
||||
|
||||
trades = pd.DataFrame([(t.pair, t.calc_profit(),
|
||||
columns = ["pair", "open_time", "close_time", "profit", "profitperc",
|
||||
"open_rate", "close_rate", "amount", "duration", "sell_reason",
|
||||
"fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
|
||||
"stake_amount", "max_rate", "min_rate", "id", "exchange",
|
||||
"stop_loss", "initial_stop_loss", "strategy", "ticker_interval"]
|
||||
|
||||
trades = pd.DataFrame([(t.pair,
|
||||
t.open_date.replace(tzinfo=pytz.UTC),
|
||||
t.close_date.replace(tzinfo=pytz.UTC) if t.close_date else None,
|
||||
t.open_rate, t.close_rate,
|
||||
t.close_date.timestamp() - t.open_date.timestamp()
|
||||
if t.close_date else None,
|
||||
t.calc_profit(), t.calc_profit_percent(),
|
||||
t.open_rate, t.close_rate, t.amount,
|
||||
(t.close_date.timestamp() - t.open_date.timestamp()
|
||||
if t.close_date else None),
|
||||
t.sell_reason,
|
||||
t.fee_open, t.fee_close,
|
||||
t.open_rate_requested,
|
||||
t.close_rate_requested,
|
||||
t.stake_amount,
|
||||
t.max_rate,
|
||||
t.min_rate,
|
||||
t.id, t.exchange,
|
||||
t.stop_loss, t.initial_stop_loss,
|
||||
t.strategy, t.ticker_interval
|
||||
)
|
||||
for t in Trade.query.all()],
|
||||
columns=columns)
|
||||
|
|
|
|||
|
|
@ -260,7 +260,7 @@ class Exchange(object):
|
|||
|
||||
if not self.markets:
|
||||
logger.warning('Unable to validate pairs (assuming they are correct).')
|
||||
# return
|
||||
return
|
||||
|
||||
for pair in pairs:
|
||||
# Note: ccxt has BaseCurrency/QuoteCurrency format for pairs
|
||||
|
|
@ -269,6 +269,12 @@ class Exchange(object):
|
|||
raise OperationalException(
|
||||
f'Pair {pair} is not available on {self.name}. '
|
||||
f'Please remove {pair} from your whitelist.')
|
||||
elif self.markets[pair].get('info', {}).get('IsRestricted', False):
|
||||
# Warn users about restricted pairs in whitelist.
|
||||
# We cannot determine reliably if Users are affected.
|
||||
logger.warning(f"Pair {pair} is restricted for some users on this exchange."
|
||||
f"Please check if you are impacted by this restriction "
|
||||
f"on the exchange and eventually remove {pair} from your whitelist.")
|
||||
|
||||
def get_valid_pair_combination(self, curr_1, curr_2) -> str:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ import sys
|
|||
from operator import itemgetter
|
||||
from pathlib import Path
|
||||
from pprint import pprint
|
||||
from typing import Any, Dict, List
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects, cpu_count
|
||||
from pandas import DataFrame
|
||||
|
|
@ -70,7 +70,7 @@ class Hyperopt(Backtesting):
|
|||
if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
|
||||
self.advise_sell = self.custom_hyperopt.populate_sell_trend # type: ignore
|
||||
|
||||
# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
|
||||
# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
|
||||
if self.config.get('use_max_market_positions', True):
|
||||
self.max_open_trades = self.config['max_open_trades']
|
||||
else:
|
||||
|
|
@ -138,11 +138,20 @@ class Hyperopt(Backtesting):
|
|||
params = best_result['params']
|
||||
|
||||
log_str = self.format_results_logstring(best_result)
|
||||
print(f"\nBest result:\n{log_str}\nwith values:")
|
||||
pprint(params, indent=4)
|
||||
print(f"\nBest result:\n\n{log_str}\n")
|
||||
if self.has_space('buy'):
|
||||
print('Buy hyperspace params:')
|
||||
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('buy')},
|
||||
indent=4)
|
||||
if self.has_space('sell'):
|
||||
print('Sell hyperspace params:')
|
||||
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('sell')},
|
||||
indent=4)
|
||||
if self.has_space('roi'):
|
||||
print("ROI table:")
|
||||
pprint(self.custom_hyperopt.generate_roi_table(params), indent=4)
|
||||
if self.has_space('stoploss'):
|
||||
print(f"Stoploss: {params.get('stoploss')}")
|
||||
|
||||
def log_results(self, results) -> None:
|
||||
"""
|
||||
|
|
@ -176,21 +185,24 @@ class Hyperopt(Backtesting):
|
|||
"""
|
||||
return any(s in self.config['spaces'] for s in [space, 'all'])
|
||||
|
||||
def hyperopt_space(self) -> List[Dimension]:
|
||||
def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
|
||||
"""
|
||||
Return the space to use during Hyperopt
|
||||
Return the dimensions in the hyperoptimization space.
|
||||
:param space: Defines hyperspace to return dimensions for.
|
||||
If None, then the self.has_space() will be used to return dimensions
|
||||
for all hyperspaces used.
|
||||
"""
|
||||
spaces: List[Dimension] = []
|
||||
if self.has_space('buy'):
|
||||
if space == 'buy' or (space is None and self.has_space('buy')):
|
||||
logger.debug("Hyperopt has 'buy' space")
|
||||
spaces += self.custom_hyperopt.indicator_space()
|
||||
if self.has_space('sell'):
|
||||
if space == 'sell' or (space is None and self.has_space('sell')):
|
||||
logger.debug("Hyperopt has 'sell' space")
|
||||
spaces += self.custom_hyperopt.sell_indicator_space()
|
||||
if self.has_space('roi'):
|
||||
if space == 'roi' or (space is None and self.has_space('roi')):
|
||||
logger.debug("Hyperopt has 'roi' space")
|
||||
spaces += self.custom_hyperopt.roi_space()
|
||||
if self.has_space('stoploss'):
|
||||
if space == 'stoploss' or (space is None and self.has_space('stoploss')):
|
||||
logger.debug("Hyperopt has 'stoploss' space")
|
||||
spaces += self.custom_hyperopt.stoploss_space()
|
||||
return spaces
|
||||
|
|
|
|||
|
|
@ -316,8 +316,9 @@ def store_plot_file(fig, filename: str, directory: Path, auto_open: bool = False
|
|||
:param ticker_interval: Used as part of the filename
|
||||
:return: None
|
||||
"""
|
||||
|
||||
directory.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
plot(fig, filename=str(directory.joinpath(filename)),
|
||||
_filename = directory.joinpath(filename)
|
||||
plot(fig, filename=str(_filename),
|
||||
auto_open=auto_open)
|
||||
logger.info(f"Stored plot as {_filename}")
|
||||
|
|
|
|||
|
|
@ -158,6 +158,23 @@ class IStrategy(ABC):
|
|||
"""
|
||||
Parses the given ticker history and returns a populated DataFrame
|
||||
add several TA indicators and buy signal to it
|
||||
:param dataframe: Dataframe containing ticker data
|
||||
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
|
||||
:return: DataFrame with ticker data and indicator data
|
||||
"""
|
||||
logger.debug("TA Analysis Launched")
|
||||
dataframe = self.advise_indicators(dataframe, metadata)
|
||||
dataframe = self.advise_buy(dataframe, metadata)
|
||||
dataframe = self.advise_sell(dataframe, metadata)
|
||||
return dataframe
|
||||
|
||||
def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Parses the given ticker history and returns a populated DataFrame
|
||||
add several TA indicators and buy signal to it
|
||||
WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
|
||||
:param dataframe: Dataframe containing ticker data
|
||||
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
|
||||
:return: DataFrame with ticker data and indicator data
|
||||
"""
|
||||
|
||||
|
|
@ -168,10 +185,7 @@ class IStrategy(ABC):
|
|||
if (not self.process_only_new_candles or
|
||||
self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
|
||||
# Defs that only make change on new candle data.
|
||||
logger.debug("TA Analysis Launched")
|
||||
dataframe = self.advise_indicators(dataframe, metadata)
|
||||
dataframe = self.advise_buy(dataframe, metadata)
|
||||
dataframe = self.advise_sell(dataframe, metadata)
|
||||
dataframe = self.analyze_ticker(dataframe, metadata)
|
||||
self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
|
||||
else:
|
||||
logger.debug("Skipping TA Analysis for already analyzed candle")
|
||||
|
|
@ -198,7 +212,7 @@ class IStrategy(ABC):
|
|||
return False, False
|
||||
|
||||
try:
|
||||
dataframe = self.analyze_ticker(dataframe, {'pair': pair})
|
||||
dataframe = self._analyze_ticker_internal(dataframe, {'pair': pair})
|
||||
except ValueError as error:
|
||||
logger.warning(
|
||||
'Unable to analyze ticker for pair %s: %s',
|
||||
|
|
|
|||
|
|
@ -305,7 +305,7 @@ def markets():
|
|||
'max': 500000,
|
||||
},
|
||||
},
|
||||
'info': '',
|
||||
'info': {},
|
||||
},
|
||||
'TKN/BTC': {
|
||||
'id': 'tknbtc',
|
||||
|
|
@ -330,7 +330,7 @@ def markets():
|
|||
'max': 500000,
|
||||
},
|
||||
},
|
||||
'info': '',
|
||||
'info': {},
|
||||
},
|
||||
'BLK/BTC': {
|
||||
'id': 'blkbtc',
|
||||
|
|
@ -355,7 +355,7 @@ def markets():
|
|||
'max': 500000,
|
||||
},
|
||||
},
|
||||
'info': '',
|
||||
'info': {},
|
||||
},
|
||||
'LTC/BTC': {
|
||||
'id': 'ltcbtc',
|
||||
|
|
@ -380,7 +380,7 @@ def markets():
|
|||
'max': 500000,
|
||||
},
|
||||
},
|
||||
'info': '',
|
||||
'info': {},
|
||||
},
|
||||
'XRP/BTC': {
|
||||
'id': 'xrpbtc',
|
||||
|
|
@ -405,7 +405,7 @@ def markets():
|
|||
'max': 500000,
|
||||
},
|
||||
},
|
||||
'info': '',
|
||||
'info': {},
|
||||
},
|
||||
'NEO/BTC': {
|
||||
'id': 'neobtc',
|
||||
|
|
@ -430,7 +430,7 @@ def markets():
|
|||
'max': 500000,
|
||||
},
|
||||
},
|
||||
'info': '',
|
||||
'info': {},
|
||||
},
|
||||
'BTT/BTC': {
|
||||
'id': 'BTTBTC',
|
||||
|
|
@ -458,7 +458,7 @@ def markets():
|
|||
'max': None
|
||||
}
|
||||
},
|
||||
'info': "",
|
||||
'info': {},
|
||||
},
|
||||
'ETH/USDT': {
|
||||
'id': 'USDT-ETH',
|
||||
|
|
@ -480,7 +480,7 @@ def markets():
|
|||
}
|
||||
},
|
||||
'active': True,
|
||||
'info': ""
|
||||
'info': {},
|
||||
},
|
||||
'LTC/USDT': {
|
||||
'id': 'USDT-LTC',
|
||||
|
|
@ -502,7 +502,7 @@ def markets():
|
|||
'max': None
|
||||
}
|
||||
},
|
||||
'info': ""
|
||||
'info': {},
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -45,6 +45,11 @@ def test_load_trades_db(default_conf, fee, mocker):
|
|||
assert isinstance(trades, DataFrame)
|
||||
assert "pair" in trades.columns
|
||||
assert "open_time" in trades.columns
|
||||
assert "profitperc" in trades.columns
|
||||
|
||||
for col in BT_DATA_COLUMNS:
|
||||
if col not in ['index', 'open_at_end']:
|
||||
assert col in trades.columns
|
||||
|
||||
|
||||
def test_extract_trades_of_period():
|
||||
|
|
|
|||
|
|
@ -318,7 +318,7 @@ def test__reload_markets_exception(default_conf, mocker, caplog):
|
|||
def test_validate_pairs(default_conf, mocker): # test exchange.validate_pairs directly
|
||||
api_mock = MagicMock()
|
||||
type(api_mock).markets = PropertyMock(return_value={
|
||||
'ETH/BTC': '', 'LTC/BTC': '', 'XRP/BTC': '', 'NEO/BTC': ''
|
||||
'ETH/BTC': {}, 'LTC/BTC': {}, 'XRP/BTC': {}, 'NEO/BTC': {}
|
||||
})
|
||||
id_mock = PropertyMock(return_value='test_exchange')
|
||||
type(api_mock).id = id_mock
|
||||
|
|
@ -332,7 +332,7 @@ def test_validate_pairs(default_conf, mocker): # test exchange.validate_pairs d
|
|||
def test_validate_pairs_not_available(default_conf, mocker):
|
||||
api_mock = MagicMock()
|
||||
type(api_mock).markets = PropertyMock(return_value={
|
||||
'XRP/BTC': 'inactive'
|
||||
'XRP/BTC': {'inactive': True}
|
||||
})
|
||||
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
|
||||
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
|
||||
|
|
@ -361,6 +361,23 @@ def test_validate_pairs_exception(default_conf, mocker, caplog):
|
|||
caplog.record_tuples)
|
||||
|
||||
|
||||
def test_validate_pairs_restricted(default_conf, mocker, caplog):
|
||||
api_mock = MagicMock()
|
||||
type(api_mock).markets = PropertyMock(return_value={
|
||||
'ETH/BTC': {}, 'LTC/BTC': {}, 'NEO/BTC': {},
|
||||
'XRP/BTC': {'info': {'IsRestricted': True}}
|
||||
})
|
||||
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
|
||||
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
|
||||
mocker.patch('freqtrade.exchange.Exchange._load_async_markets', MagicMock())
|
||||
|
||||
Exchange(default_conf)
|
||||
assert log_has(f"Pair XRP/BTC is restricted for some users on this exchange."
|
||||
f"Please check if you are impacted by this restriction "
|
||||
f"on the exchange and eventually remove XRP/BTC from your whitelist.",
|
||||
caplog.record_tuples)
|
||||
|
||||
|
||||
def test_validate_timeframes(default_conf, mocker):
|
||||
default_conf["ticker_interval"] = "5m"
|
||||
api_mock = MagicMock()
|
||||
|
|
|
|||
|
|
@ -202,6 +202,7 @@ def test_setup_configuration_without_arguments(mocker, default_conf, caplog) ->
|
|||
assert config['runmode'] == RunMode.BACKTEST
|
||||
|
||||
|
||||
@pytest.mark.filterwarnings("ignore:DEPRECATED")
|
||||
def test_setup_bt_configuration_with_arguments(mocker, default_conf, caplog) -> None:
|
||||
patched_configuration_load_config_file(mocker, default_conf)
|
||||
mocker.patch(
|
||||
|
|
@ -812,6 +813,7 @@ def test_backtest_record(default_conf, fee, mocker):
|
|||
assert dur > 0
|
||||
|
||||
|
||||
@pytest.mark.filterwarnings("ignore:DEPRECATED")
|
||||
def test_backtest_start_live(default_conf, mocker, caplog):
|
||||
default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
|
||||
|
||||
|
|
@ -858,6 +860,7 @@ def test_backtest_start_live(default_conf, mocker, caplog):
|
|||
assert log_has(line, caplog.record_tuples)
|
||||
|
||||
|
||||
@pytest.mark.filterwarnings("ignore:DEPRECATED")
|
||||
def test_backtest_start_multi_strat(default_conf, mocker, caplog):
|
||||
default_conf['exchange']['pair_whitelist'] = ['UNITTEST/BTC']
|
||||
|
||||
|
|
|
|||
|
|
@ -466,7 +466,7 @@ def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
|
|||
parallel.assert_called_once()
|
||||
|
||||
out, err = capsys.readouterr()
|
||||
assert 'Best result:\n* 1/1: foo result Objective: 1.00000\nwith values:\n' in out
|
||||
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
|
||||
assert dumper.called
|
||||
# Should be called twice, once for tickerdata, once to save evaluations
|
||||
assert dumper.call_count == 2
|
||||
|
|
|
|||
|
|
@ -19,13 +19,13 @@ _STRATEGY = DefaultStrategy(config={})
|
|||
|
||||
def test_returns_latest_buy_signal(mocker, default_conf, ticker_history):
|
||||
mocker.patch.object(
|
||||
_STRATEGY, 'analyze_ticker',
|
||||
_STRATEGY, '_analyze_ticker_internal',
|
||||
return_value=DataFrame([{'buy': 1, 'sell': 0, 'date': arrow.utcnow()}])
|
||||
)
|
||||
assert _STRATEGY.get_signal('ETH/BTC', '5m', ticker_history) == (True, False)
|
||||
|
||||
mocker.patch.object(
|
||||
_STRATEGY, 'analyze_ticker',
|
||||
_STRATEGY, '_analyze_ticker_internal',
|
||||
return_value=DataFrame([{'buy': 0, 'sell': 1, 'date': arrow.utcnow()}])
|
||||
)
|
||||
assert _STRATEGY.get_signal('ETH/BTC', '5m', ticker_history) == (False, True)
|
||||
|
|
@ -33,14 +33,14 @@ def test_returns_latest_buy_signal(mocker, default_conf, ticker_history):
|
|||
|
||||
def test_returns_latest_sell_signal(mocker, default_conf, ticker_history):
|
||||
mocker.patch.object(
|
||||
_STRATEGY, 'analyze_ticker',
|
||||
_STRATEGY, '_analyze_ticker_internal',
|
||||
return_value=DataFrame([{'sell': 1, 'buy': 0, 'date': arrow.utcnow()}])
|
||||
)
|
||||
|
||||
assert _STRATEGY.get_signal('ETH/BTC', '5m', ticker_history) == (False, True)
|
||||
|
||||
mocker.patch.object(
|
||||
_STRATEGY, 'analyze_ticker',
|
||||
_STRATEGY, '_analyze_ticker_internal',
|
||||
return_value=DataFrame([{'sell': 0, 'buy': 1, 'date': arrow.utcnow()}])
|
||||
)
|
||||
assert _STRATEGY.get_signal('ETH/BTC', '5m', ticker_history) == (True, False)
|
||||
|
|
@ -60,7 +60,7 @@ def test_get_signal_empty(default_conf, mocker, caplog):
|
|||
def test_get_signal_exception_valueerror(default_conf, mocker, caplog, ticker_history):
|
||||
caplog.set_level(logging.INFO)
|
||||
mocker.patch.object(
|
||||
_STRATEGY, 'analyze_ticker',
|
||||
_STRATEGY, '_analyze_ticker_internal',
|
||||
side_effect=ValueError('xyz')
|
||||
)
|
||||
assert (False, False) == _STRATEGY.get_signal('foo', default_conf['ticker_interval'],
|
||||
|
|
@ -71,7 +71,7 @@ def test_get_signal_exception_valueerror(default_conf, mocker, caplog, ticker_hi
|
|||
def test_get_signal_empty_dataframe(default_conf, mocker, caplog, ticker_history):
|
||||
caplog.set_level(logging.INFO)
|
||||
mocker.patch.object(
|
||||
_STRATEGY, 'analyze_ticker',
|
||||
_STRATEGY, '_analyze_ticker_internal',
|
||||
return_value=DataFrame([])
|
||||
)
|
||||
assert (False, False) == _STRATEGY.get_signal('xyz', default_conf['ticker_interval'],
|
||||
|
|
@ -86,7 +86,7 @@ def test_get_signal_old_dataframe(default_conf, mocker, caplog, ticker_history):
|
|||
oldtime = arrow.utcnow().shift(minutes=-16)
|
||||
ticks = DataFrame([{'buy': 1, 'date': oldtime}])
|
||||
mocker.patch.object(
|
||||
_STRATEGY, 'analyze_ticker',
|
||||
_STRATEGY, '_analyze_ticker_internal',
|
||||
return_value=DataFrame(ticks)
|
||||
)
|
||||
assert (False, False) == _STRATEGY.get_signal('xyz', default_conf['ticker_interval'],
|
||||
|
|
@ -252,7 +252,7 @@ def test_analyze_ticker_default(ticker_history, mocker, caplog) -> None:
|
|||
caplog.record_tuples)
|
||||
|
||||
|
||||
def test_analyze_ticker_skip_analyze(ticker_history, mocker, caplog) -> None:
|
||||
def test__analyze_ticker_internal_skip_analyze(ticker_history, mocker, caplog) -> None:
|
||||
caplog.set_level(logging.DEBUG)
|
||||
ind_mock = MagicMock(side_effect=lambda x, meta: x)
|
||||
buy_mock = MagicMock(side_effect=lambda x, meta: x)
|
||||
|
|
@ -267,7 +267,7 @@ def test_analyze_ticker_skip_analyze(ticker_history, mocker, caplog) -> None:
|
|||
strategy = DefaultStrategy({})
|
||||
strategy.process_only_new_candles = True
|
||||
|
||||
ret = strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
|
||||
ret = strategy._analyze_ticker_internal(ticker_history, {'pair': 'ETH/BTC'})
|
||||
assert 'high' in ret.columns
|
||||
assert 'low' in ret.columns
|
||||
assert 'close' in ret.columns
|
||||
|
|
@ -280,7 +280,7 @@ def test_analyze_ticker_skip_analyze(ticker_history, mocker, caplog) -> None:
|
|||
caplog.record_tuples)
|
||||
caplog.clear()
|
||||
|
||||
ret = strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
|
||||
ret = strategy._analyze_ticker_internal(ticker_history, {'pair': 'ETH/BTC'})
|
||||
# No analysis happens as process_only_new_candles is true
|
||||
assert ind_mock.call_count == 1
|
||||
assert buy_mock.call_count == 1
|
||||
|
|
|
|||
|
|
@ -327,6 +327,7 @@ def test_setup_configuration_without_arguments(mocker, default_conf, caplog) ->
|
|||
assert 'export' not in config
|
||||
|
||||
|
||||
@pytest.mark.filterwarnings("ignore:DEPRECATED")
|
||||
def test_setup_configuration_with_arguments(mocker, default_conf, caplog) -> None:
|
||||
patched_configuration_load_config_file(mocker, default_conf)
|
||||
mocker.patch(
|
||||
|
|
|
|||
|
|
@ -217,6 +217,8 @@ def test_generate_plot_file(mocker, caplog):
|
|||
assert plot_mock.call_args[0][0] == fig
|
||||
assert (plot_mock.call_args_list[0][1]['filename']
|
||||
== "user_data/plots/freqtrade-plot-UNITTEST_BTC-5m.html")
|
||||
assert log_has("Stored plot as user_data/plots/freqtrade-plot-UNITTEST_BTC-5m.html",
|
||||
caplog.record_tuples)
|
||||
|
||||
|
||||
def test_add_profit():
|
||||
|
|
|
|||
|
|
@ -16,8 +16,6 @@ import logging
|
|||
import sys
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from freqtrade.configuration import Arguments
|
||||
from freqtrade.configuration.arguments import ARGS_PLOT_DATAFRAME
|
||||
from freqtrade.data.btanalysis import extract_trades_of_period
|
||||
|
|
@ -30,20 +28,6 @@ from freqtrade.state import RunMode
|
|||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def generate_dataframe(strategy, tickers, pair) -> pd.DataFrame:
|
||||
"""
|
||||
Get tickers then Populate strategy indicators and signals, then return the full dataframe
|
||||
:return: the DataFrame of a pair
|
||||
"""
|
||||
|
||||
dataframes = strategy.tickerdata_to_dataframe(tickers)
|
||||
dataframe = dataframes[pair]
|
||||
dataframe = strategy.advise_buy(dataframe, {'pair': pair})
|
||||
dataframe = strategy.advise_sell(dataframe, {'pair': pair})
|
||||
|
||||
return dataframe
|
||||
|
||||
|
||||
def analyse_and_plot_pairs(config: Dict[str, Any]):
|
||||
"""
|
||||
From arguments provided in cli:
|
||||
|
|
@ -57,6 +41,7 @@ def analyse_and_plot_pairs(config: Dict[str, Any]):
|
|||
"""
|
||||
plot_elements = init_plotscript(config)
|
||||
trades = plot_elements['trades']
|
||||
strategy = plot_elements["strategy"]
|
||||
|
||||
pair_counter = 0
|
||||
for pair, data in plot_elements["tickers"].items():
|
||||
|
|
@ -64,7 +49,8 @@ def analyse_and_plot_pairs(config: Dict[str, Any]):
|
|||
logger.info("analyse pair %s", pair)
|
||||
tickers = {}
|
||||
tickers[pair] = data
|
||||
dataframe = generate_dataframe(plot_elements["strategy"], tickers, pair)
|
||||
|
||||
dataframe = strategy.analyze_ticker(tickers[pair], {'pair': pair})
|
||||
|
||||
trades_pair = trades.loc[trades['pair'] == pair]
|
||||
trades_pair = extract_trades_of_period(dataframe, trades_pair)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user