Merge branch 'develop' into cleaner-tests
This commit is contained in:
Janne Sinivirta
GitHub
@@ -49,6 +49,52 @@ def test_init_exception(default_conf, mocker):
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Exchange(default_conf)
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def test_symbol_amount_prec(default_conf, mocker):
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'''
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Test rounds down to 4 Decimal places
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'''
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api_mock = MagicMock()
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api_mock.load_markets = MagicMock(return_value={
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'ETH/BTC': '', 'LTC/BTC': '', 'XRP/BTC': '', 'NEO/BTC': ''
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})
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mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value='binance'))
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markets = PropertyMock(return_value={'ETH/BTC': {'precision': {'amount': 4}}})
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type(api_mock).markets = markets
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mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
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mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
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exchange = Exchange(default_conf)
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amount = 2.34559
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pair = 'ETH/BTC'
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amount = exchange.symbol_amount_prec(pair, amount)
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assert amount == 2.3455
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def test_symbol_price_prec(default_conf, mocker):
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'''
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Test rounds up to 4 decimal places
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'''
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api_mock = MagicMock()
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api_mock.load_markets = MagicMock(return_value={
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'ETH/BTC': '', 'LTC/BTC': '', 'XRP/BTC': '', 'NEO/BTC': ''
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})
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mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value='binance'))
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markets = PropertyMock(return_value={'ETH/BTC': {'precision': {'price': 4}}})
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type(api_mock).markets = markets
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mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
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mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
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exchange = Exchange(default_conf)
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price = 2.34559
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pair = 'ETH/BTC'
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price = exchange.symbol_price_prec(pair, price)
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assert price == 2.3456
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def test_validate_pairs(default_conf, mocker):
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api_mock = MagicMock()
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api_mock.load_markets = MagicMock(return_value={
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@@ -145,7 +145,7 @@ def _trend(signals, buy_value, sell_value):
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return signals
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def _trend_alternate(dataframe=None):
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def _trend_alternate(dataframe=None, metadata=None):
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signals = dataframe
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low = signals['low']
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n = len(low)
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@@ -314,8 +314,8 @@ def test_backtesting_init(mocker, default_conf) -> None:
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assert backtesting.config == default_conf
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assert backtesting.ticker_interval == '5m'
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assert callable(backtesting.tickerdata_to_dataframe)
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assert callable(backtesting.populate_buy_trend)
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assert callable(backtesting.populate_sell_trend)
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assert callable(backtesting.advise_buy)
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assert callable(backtesting.advise_sell)
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get_fee.assert_called()
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assert backtesting.fee == 0.5
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@@ -562,42 +562,42 @@ def test_backtest_ticks(default_conf, fee, mocker):
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mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
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patch_exchange(mocker)
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ticks = [1, 5]
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fun = Backtesting(default_conf).populate_buy_trend
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fun = Backtesting(default_conf).advise_buy
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for _ in ticks:
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backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
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backtesting = Backtesting(default_conf)
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backtesting.populate_buy_trend = fun # Override
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backtesting.populate_sell_trend = fun # Override
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backtesting.advise_buy = fun # Override
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backtesting.advise_sell = fun # Override
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results = backtesting.backtest(backtest_conf)
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assert not results.empty
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def test_backtest_clash_buy_sell(mocker, default_conf):
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# Override the default buy trend function in our default_strategy
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def fun(dataframe=None):
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def fun(dataframe=None, pair=None):
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buy_value = 1
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sell_value = 1
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return _trend(dataframe, buy_value, sell_value)
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backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
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backtesting = Backtesting(default_conf)
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backtesting.populate_buy_trend = fun # Override
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backtesting.populate_sell_trend = fun # Override
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backtesting.advise_buy = fun # Override
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backtesting.advise_sell = fun # Override
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results = backtesting.backtest(backtest_conf)
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assert results.empty
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def test_backtest_only_sell(mocker, default_conf):
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# Override the default buy trend function in our default_strategy
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def fun(dataframe=None):
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def fun(dataframe=None, pair=None):
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buy_value = 0
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sell_value = 1
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return _trend(dataframe, buy_value, sell_value)
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backtest_conf = _make_backtest_conf(mocker, conf=default_conf)
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backtesting = Backtesting(default_conf)
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backtesting.populate_buy_trend = fun # Override
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backtesting.populate_sell_trend = fun # Override
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backtesting.advise_buy = fun # Override
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backtesting.advise_sell = fun # Override
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results = backtesting.backtest(backtest_conf)
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assert results.empty
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@@ -606,8 +606,8 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker):
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mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
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backtest_conf = _make_backtest_conf(mocker, conf=default_conf, pair='UNITTEST/BTC')
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backtesting = Backtesting(default_conf)
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backtesting.populate_buy_trend = _trend_alternate # Override
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backtesting.populate_sell_trend = _trend_alternate # Override
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backtesting.advise_buy = _trend_alternate # Override
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backtesting.advise_sell = _trend_alternate # Override
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results = backtesting.backtest(backtest_conf)
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backtesting._store_backtest_result("test_.json", results)
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assert len(results) == 4
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@@ -100,7 +100,7 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
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}
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)
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out, err = capsys.readouterr()
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assert ' 1/2: foo. Loss 1.00000'in out
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assert ' 1/2: foo. Loss 1.00000' in out
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def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
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@@ -218,7 +218,7 @@ def test_populate_indicators(hyperopt) -> None:
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tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
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tickerlist = {'UNITTEST/BTC': tick}
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dataframes = hyperopt.tickerdata_to_dataframe(tickerlist)
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dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'])
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dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
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# Check if some indicators are generated. We will not test all of them
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assert 'adx' in dataframe
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@@ -230,7 +230,7 @@ def test_buy_strategy_generator(hyperopt) -> None:
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tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
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tickerlist = {'UNITTEST/BTC': tick}
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dataframes = hyperopt.tickerdata_to_dataframe(tickerlist)
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dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'])
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dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
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populate_buy_trend = hyperopt.buy_strategy_generator(
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{
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@@ -245,7 +245,7 @@ def test_buy_strategy_generator(hyperopt) -> None:
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'trigger': 'bb_lower'
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}
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)
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result = populate_buy_trend(dataframe)
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result = populate_buy_trend(dataframe, {'pair': 'UNITTEST/BTC'})
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# Check if some indicators are generated. We will not test all of them
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assert 'buy' in result
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assert 1 in result['buy']
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235
freqtrade/tests/strategy/legacy_strategy.py
Normal file
235
freqtrade/tests/strategy/legacy_strategy.py
Normal file
@@ -0,0 +1,235 @@
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# --- Do not remove these libs ---
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import numpy # noqa
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# This class is a sample. Feel free to customize it.
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class TestStrategyLegacy(IStrategy):
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"""
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This is a test strategy using the legacy function headers, which will be
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removed in a future update.
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Please do not use this as a template, but refer to user_data/strategy/TestStrategy.py
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for a uptodate version of this template.
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"""
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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi"
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minimal_roi = {
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"40": 0.0,
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"30": 0.01,
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"20": 0.02,
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"0": 0.04
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}
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# Optimal stoploss designed for the strategy
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# This attribute will be overridden if the config file contains "stoploss"
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stoploss = -0.10
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# Optimal ticker interval for the strategy
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ticker_interval = '5m'
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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"""
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# Momentum Indicator
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# ------------------------------------
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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"""
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# Awesome oscillator
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dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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# Commodity Channel Index: values Oversold:<-100, Overbought:>100
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dataframe['cci'] = ta.CCI(dataframe)
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# MACD
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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# MFI
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dataframe['mfi'] = ta.MFI(dataframe)
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# Minus Directional Indicator / Movement
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dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# Plus Directional Indicator / Movement
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dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# ROC
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dataframe['roc'] = ta.ROC(dataframe)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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rsi = 0.1 * (dataframe['rsi'] - 50)
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dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
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# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
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dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
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# Stoch
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stoch = ta.STOCH(dataframe)
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dataframe['slowd'] = stoch['slowd']
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dataframe['slowk'] = stoch['slowk']
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# Stoch fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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# Stoch RSI
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stoch_rsi = ta.STOCHRSI(dataframe)
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dataframe['fastd_rsi'] = stoch_rsi['fastd']
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dataframe['fastk_rsi'] = stoch_rsi['fastk']
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"""
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# Overlap Studies
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# ------------------------------------
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# Bollinger bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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"""
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# EMA - Exponential Moving Average
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dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
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dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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# SAR Parabol
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dataframe['sar'] = ta.SAR(dataframe)
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# SMA - Simple Moving Average
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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"""
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# TEMA - Triple Exponential Moving Average
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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# Cycle Indicator
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# ------------------------------------
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# Hilbert Transform Indicator - SineWave
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hilbert = ta.HT_SINE(dataframe)
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dataframe['htsine'] = hilbert['sine']
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dataframe['htleadsine'] = hilbert['leadsine']
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# Pattern Recognition - Bullish candlestick patterns
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# ------------------------------------
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"""
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# Hammer: values [0, 100]
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dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
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# Inverted Hammer: values [0, 100]
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dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
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# Dragonfly Doji: values [0, 100]
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dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
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# Piercing Line: values [0, 100]
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dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
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# Morningstar: values [0, 100]
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dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
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# Three White Soldiers: values [0, 100]
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dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
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"""
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# Pattern Recognition - Bearish candlestick patterns
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# ------------------------------------
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"""
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# Hanging Man: values [0, 100]
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dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
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# Shooting Star: values [0, 100]
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dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
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# Gravestone Doji: values [0, 100]
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dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
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# Dark Cloud Cover: values [0, 100]
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dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
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# Evening Doji Star: values [0, 100]
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dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
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# Evening Star: values [0, 100]
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dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
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"""
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# Pattern Recognition - Bullish/Bearish candlestick patterns
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# ------------------------------------
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"""
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# Three Line Strike: values [0, -100, 100]
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dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
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# Spinning Top: values [0, -100, 100]
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dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
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# Engulfing: values [0, -100, 100]
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dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
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# Harami: values [0, -100, 100]
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dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
|
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# Three Outside Up/Down: values [0, -100, 100]
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dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
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# Three Inside Up/Down: values [0, -100, 100]
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dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
|
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"""
|
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|
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# Chart type
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# ------------------------------------
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"""
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||||
# Heikinashi stategy
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heikinashi = qtpylib.heikinashi(dataframe)
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dataframe['ha_open'] = heikinashi['open']
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dataframe['ha_close'] = heikinashi['close']
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dataframe['ha_high'] = heikinashi['high']
|
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dataframe['ha_low'] = heikinashi['low']
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"""
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
|
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"""
|
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
|
||||
"""
|
||||
dataframe.loc[
|
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(
|
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(dataframe['adx'] > 30) &
|
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(dataframe['tema'] <= dataframe['bb_middleband']) &
|
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(dataframe['tema'] > dataframe['tema'].shift(1))
|
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),
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'buy'] = 1
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return dataframe
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|
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def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
|
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"""
|
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Based on TA indicators, populates the sell signal for the given dataframe
|
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:param dataframe: DataFrame
|
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:return: DataFrame with buy column
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
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(dataframe['adx'] > 70) &
|
||||
(dataframe['tema'] > dataframe['bb_middleband']) &
|
||||
(dataframe['tema'] < dataframe['tema'].shift(1))
|
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),
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'sell'] = 1
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return dataframe
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||||
@@ -25,10 +25,11 @@ def test_default_strategy_structure():
|
||||
def test_default_strategy(result):
|
||||
strategy = DefaultStrategy({})
|
||||
|
||||
metadata = {'pair': 'ETH/BTC'}
|
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assert type(strategy.minimal_roi) is dict
|
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assert type(strategy.stoploss) is float
|
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assert type(strategy.ticker_interval) is str
|
||||
indicators = strategy.populate_indicators(result)
|
||||
indicators = strategy.populate_indicators(result, metadata)
|
||||
assert type(indicators) is DataFrame
|
||||
assert type(strategy.populate_buy_trend(indicators)) is DataFrame
|
||||
assert type(strategy.populate_sell_trend(indicators)) is DataFrame
|
||||
assert type(strategy.populate_buy_trend(indicators, metadata)) is DataFrame
|
||||
assert type(strategy.populate_sell_trend(indicators, metadata)) is DataFrame
|
||||
|
||||
@@ -1,8 +1,10 @@
|
||||
# pragma pylint: disable=missing-docstring, protected-access, C0103
|
||||
import logging
|
||||
import os
|
||||
from os import path
|
||||
import warnings
|
||||
|
||||
import pytest
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.strategy import import_strategy
|
||||
from freqtrade.strategy.default_strategy import DefaultStrategy
|
||||
@@ -37,8 +39,8 @@ def test_import_strategy(caplog):
|
||||
|
||||
def test_search_strategy():
|
||||
default_config = {}
|
||||
default_location = os.path.join(os.path.dirname(
|
||||
os.path.realpath(__file__)), '..', '..', 'strategy'
|
||||
default_location = path.join(path.dirname(
|
||||
path.realpath(__file__)), '..', '..', 'strategy'
|
||||
)
|
||||
assert isinstance(
|
||||
StrategyResolver._search_strategy(
|
||||
@@ -57,12 +59,13 @@ def test_search_strategy():
|
||||
|
||||
def test_load_strategy(result):
|
||||
resolver = StrategyResolver({'strategy': 'TestStrategy'})
|
||||
assert 'adx' in resolver.strategy.populate_indicators(result)
|
||||
metadata = {'pair': 'ETH/BTC'}
|
||||
assert 'adx' in resolver.strategy.advise_indicators(result, metadata=metadata)
|
||||
|
||||
|
||||
def test_load_strategy_invalid_directory(result, caplog):
|
||||
resolver = StrategyResolver()
|
||||
extra_dir = os.path.join('some', 'path')
|
||||
extra_dir = path.join('some', 'path')
|
||||
resolver._load_strategy('TestStrategy', config={}, extra_dir=extra_dir)
|
||||
|
||||
assert (
|
||||
@@ -70,7 +73,8 @@ def test_load_strategy_invalid_directory(result, caplog):
|
||||
logging.WARNING,
|
||||
'Path "{}" does not exist'.format(extra_dir),
|
||||
) in caplog.record_tuples
|
||||
assert 'adx' in resolver.strategy.populate_indicators(result)
|
||||
|
||||
assert 'adx' in resolver.strategy.advise_indicators(result, {'pair': 'ETH/BTC'})
|
||||
|
||||
|
||||
def test_load_not_found_strategy():
|
||||
@@ -85,7 +89,7 @@ def test_strategy(result):
|
||||
config = {'strategy': 'DefaultStrategy'}
|
||||
|
||||
resolver = StrategyResolver(config)
|
||||
|
||||
metadata = {'pair': 'ETH/BTC'}
|
||||
assert resolver.strategy.minimal_roi[0] == 0.04
|
||||
assert config["minimal_roi"]['0'] == 0.04
|
||||
|
||||
@@ -95,12 +99,13 @@ def test_strategy(result):
|
||||
assert resolver.strategy.ticker_interval == '5m'
|
||||
assert config['ticker_interval'] == '5m'
|
||||
|
||||
assert 'adx' in resolver.strategy.populate_indicators(result)
|
||||
df_indicators = resolver.strategy.advise_indicators(result, metadata=metadata)
|
||||
assert 'adx' in df_indicators
|
||||
|
||||
dataframe = resolver.strategy.populate_buy_trend(resolver.strategy.populate_indicators(result))
|
||||
dataframe = resolver.strategy.advise_buy(df_indicators, metadata=metadata)
|
||||
assert 'buy' in dataframe.columns
|
||||
|
||||
dataframe = resolver.strategy.populate_sell_trend(resolver.strategy.populate_indicators(result))
|
||||
dataframe = resolver.strategy.advise_sell(df_indicators, metadata=metadata)
|
||||
assert 'sell' in dataframe.columns
|
||||
|
||||
|
||||
@@ -150,3 +155,59 @@ def test_strategy_override_ticker_interval(caplog):
|
||||
logging.INFO,
|
||||
'Override strategy \'ticker_interval\' with value in config file: 60.'
|
||||
) in caplog.record_tuples
|
||||
|
||||
|
||||
def test_deprecate_populate_indicators(result):
|
||||
default_location = path.join(path.dirname(path.realpath(__file__)))
|
||||
resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
|
||||
'strategy_path': default_location})
|
||||
with warnings.catch_warnings(record=True) as w:
|
||||
# Cause all warnings to always be triggered.
|
||||
warnings.simplefilter("always")
|
||||
indicators = resolver.strategy.advise_indicators(result, 'ETH/BTC')
|
||||
assert len(w) == 1
|
||||
assert issubclass(w[-1].category, DeprecationWarning)
|
||||
assert "deprecated - check out the Sample strategy to see the current function headers!" \
|
||||
in str(w[-1].message)
|
||||
|
||||
with warnings.catch_warnings(record=True) as w:
|
||||
# Cause all warnings to always be triggered.
|
||||
warnings.simplefilter("always")
|
||||
resolver.strategy.advise_buy(indicators, 'ETH/BTC')
|
||||
assert len(w) == 1
|
||||
assert issubclass(w[-1].category, DeprecationWarning)
|
||||
assert "deprecated - check out the Sample strategy to see the current function headers!" \
|
||||
in str(w[-1].message)
|
||||
|
||||
with warnings.catch_warnings(record=True) as w:
|
||||
# Cause all warnings to always be triggered.
|
||||
warnings.simplefilter("always")
|
||||
resolver.strategy.advise_sell(indicators, 'ETH_BTC')
|
||||
assert len(w) == 1
|
||||
assert issubclass(w[-1].category, DeprecationWarning)
|
||||
assert "deprecated - check out the Sample strategy to see the current function headers!" \
|
||||
in str(w[-1].message)
|
||||
|
||||
|
||||
def test_call_deprecated_function(result, monkeypatch):
|
||||
default_location = path.join(path.dirname(path.realpath(__file__)))
|
||||
resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
|
||||
'strategy_path': default_location})
|
||||
metadata = {'pair': 'ETH/BTC'}
|
||||
|
||||
# Make sure we are using a legacy function
|
||||
assert resolver.strategy._populate_fun_len == 2
|
||||
assert resolver.strategy._buy_fun_len == 2
|
||||
assert resolver.strategy._sell_fun_len == 2
|
||||
|
||||
indicator_df = resolver.strategy.advise_indicators(result, metadata=metadata)
|
||||
assert type(indicator_df) is DataFrame
|
||||
assert 'adx' in indicator_df.columns
|
||||
|
||||
buydf = resolver.strategy.advise_buy(result, metadata=metadata)
|
||||
assert type(buydf) is DataFrame
|
||||
assert 'buy' in buydf.columns
|
||||
|
||||
selldf = resolver.strategy.advise_sell(result, metadata=metadata)
|
||||
assert type(selldf) is DataFrame
|
||||
assert 'sell' in selldf
|
||||
|
||||
@@ -14,7 +14,7 @@ def load_dataframe_pair(pairs, strategy):
|
||||
assert isinstance(pairs[0], str)
|
||||
dataframe = ld[pairs[0]]
|
||||
|
||||
dataframe = strategy.analyze_ticker(dataframe)
|
||||
dataframe = strategy.analyze_ticker(dataframe, pairs[0])
|
||||
return dataframe
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user