import numpy as np import pandas as pd import pytest from freqtrade.constants import DEFAULT_TRADES_COLUMNS from freqtrade.data.converter import populate_dataframe_with_trades from freqtrade.data.converter.orderflow import trades_to_volumeprofile_with_total_delta_bid_ask from freqtrade.data.converter.trade_converter import trades_list_to_df BIN_SIZE_SCALE = 0.5 def read_csv(filename, converter_columns: list = ['side', 'type']): return pd.read_csv(filename, skipinitialspace=True, infer_datetime_format=True, index_col=0, parse_dates=True, converters={col: str.strip for col in converter_columns}) @pytest.fixture(scope="module") def populate_dataframe_with_trades_dataframe(): return pd.read_feather('tests/testdata/populate_dataframe_with_trades_DF.feather') @pytest.fixture(scope="module") def populate_dataframe_with_trades_trades(): return pd.read_feather('tests/testdata/populate_dataframe_with_trades_TRADES.feather') @pytest.fixture(scope="module") def candles(): return pd.read_json('tests/testdata/candles.json').copy() @pytest.fixture(scope="module") def public_trades_list(): return read_csv('tests/testdata/public_trades_list.csv').copy() @pytest.fixture(scope="module") def public_trades_list_simple(): return read_csv('tests/testdata/public_trades_list_simple_example.csv').copy() @pytest.fixture(scope="module") def public_trades_list_simple_results(): return read_csv('tests/testdata/public_trades_list_simple_results.csv').copy() @pytest.fixture(scope="module") def public_trades_list_simple_bidask(): return read_csv('tests/testdata/public_trades_list_simple_bidask.csv').copy() def test_public_trades_columns_before_change( populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades): assert populate_dataframe_with_trades_dataframe.columns.tolist() == [ 'date', 'open', 'high', 'low', 'close', 'volume'] assert populate_dataframe_with_trades_trades.columns.tolist() == [ 'timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost', 'date'] def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow( populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades): dataframe = populate_dataframe_with_trades_dataframe.copy() trades = populate_dataframe_with_trades_trades.copy() dataframe['date'] = pd.to_datetime( dataframe['date'], unit='ms') dataframe = dataframe.copy().tail().reset_index(drop=True) config = {'timeframe': '5m', 'orderflow': { 'scale': 0.005, 'imbalance_volume': 0, 'imbalance_ratio': 300, 'stacked_imbalance_range': 3 }} df = populate_dataframe_with_trades(config, dataframe, trades, pair='unitttest') results = df.iloc[0] t = results['trades'] of = results['orderflow'] assert 0 != len(results) # 13 columns assert 151 == len(t) # orderflow/cluster/footprint assert 23 == len(of) assert [0.0, 1.0, 4.999, 0.0, 4.999, 4.999, 1.0] == of.iloc[0].values.tolist() assert [0.0, 1.0, 0.103, 0.0, 0.103, 0.103, 1.0] == of.iloc[-1].values.tolist() of = df.iloc[-1]['orderflow'] assert 19 == len(of) assert [1.0, 0.0, -12.536, 12.536, 0.0, 12.536, 1.0] == of.iloc[0].values.tolist() assert [4.0, 3.0, -40.94800000000001, 59.18200000000001, 18.233999999999998, 77.41600000000001, 7.0] == of.iloc[-1].values.tolist() assert -50.519000000000005 == results['delta'] assert -79.469 == results['min_delta'] assert 17.298 == results['max_delta'] assert np.isnan(results['stacked_imbalances_bid']) assert np.isnan(results['stacked_imbalances_ask']) results = df.iloc[-3] assert -112.71399999999994 == results['delta'] assert -120.673 == results['min_delta'] assert 11.664 == results['max_delta'] assert np.isnan(results['stacked_imbalances_bid']) assert np.isnan(results['stacked_imbalances_ask']) results = df.iloc[-1] assert -49.30200000000002 == results['delta'] assert -70.222 == results['min_delta'] assert 11.213000000000003 == results['max_delta'] assert np.isnan(results['stacked_imbalances_bid']) assert np.isnan(results['stacked_imbalances_ask']) def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades( populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades): dataframe = populate_dataframe_with_trades_dataframe.copy() trades = populate_dataframe_with_trades_trades.copy() # slice of unnecessary trades dataframe['date'] = pd.to_datetime( dataframe['date'], unit='ms') dataframe = dataframe.copy().tail().reset_index(drop=True) trades = trades.copy().loc[trades.date >= dataframe.date[0]] trades.reset_index(inplace=True, drop=True) assert trades['id'][0] == '313881442' config = { 'timeframe': '5m', 'orderflow': { 'scale': 0.5, 'imbalance_volume': 0, 'imbalance_ratio': 300, 'stacked_imbalance_range': 3 } } df = populate_dataframe_with_trades(config, dataframe, trades, pair='unitttest') row = df.iloc[0] assert list(df.columns) == ['date', 'open', 'high', 'low', 'close', 'volume', 'trades', 'orderflow', 'bid', 'ask', 'delta', 'min_delta', 'max_delta', 'total_trades', 'stacked_imbalances_bid', 'stacked_imbalances_ask'] assert -50.519 == pytest.approx(row['delta']) assert 219.961 == row['bid'] assert 169.442 == row['ask'] assert 151 == len(row.trades) t = row['trades'].iloc[0] assert trades['id'][0] == t["id"] assert int(trades['timestamp'][0]) == int(t['timestamp']) assert 'sell' == t['side'] assert '313881442' == t['id'] assert 234.72 == t['price'] def test_public_trades_put_volume_profile_into_ohlcv_candles(public_trades_list_simple, candles): df = trades_list_to_df( public_trades_list_simple[DEFAULT_TRADES_COLUMNS].values.tolist()) df = trades_to_volumeprofile_with_total_delta_bid_ask( df, scale=BIN_SIZE_SCALE) candles['vp'] = np.nan candles.loc[candles.index == 1, ['vp']] = candles.loc[candles.index == 1, [ 'vp']].applymap(lambda x: pd.DataFrame(df.to_dict())) assert 0.14 == candles['vp'][1].values.tolist()[1][2] # delta assert 0.14 == candles['vp'][1]['delta'].iat[1] def test_public_trades_binned_big_sample_list(public_trades_list): BIN_SIZE_SCALE = 0.05 trades = trades_list_to_df( public_trades_list[DEFAULT_TRADES_COLUMNS].values.tolist()) df = trades_to_volumeprofile_with_total_delta_bid_ask( trades, scale=BIN_SIZE_SCALE) assert df.columns.tolist() == ['bid', 'ask', 'delta', 'bid_amount', 'ask_amount', 'total_volume', 'total_trades'] assert 23 == len(df) assert df.index[0] < df.index[1] < df.index[2] assert df.index[0] + BIN_SIZE_SCALE == df.index[1] assert (trades['price'].min() - BIN_SIZE_SCALE) < df.index[0] < trades['price'].max() assert (df.index[0] + BIN_SIZE_SCALE) >= df.index[1] assert (trades['price'].max() - BIN_SIZE_SCALE) < df.index[-1] < trades['price'].max() assert 32 == df['bid'].iat[0] # bid assert 197.512 == df['bid_amount'].iat[0] # bid assert 88.98 == df['ask_amount'].iat[0] # ask assert 26 == df['ask'].iat[0] # ask assert -108.532 == pytest.approx(df['delta'].iat[0]) # delta assert 3 == df['bid'].iat[-1] # bid assert 50.659 == df['bid_amount'].iat[-1] # bid assert 108.21 == df['ask_amount'].iat[-1] # ask assert 44 == df['ask'].iat[-1] # ask assert 57.551 == df['delta'].iat[-1] # delta BIN_SIZE_SCALE = 1 trades = trades_list_to_df(public_trades_list[DEFAULT_TRADES_COLUMNS].values.tolist()) df = trades_to_volumeprofile_with_total_delta_bid_ask( trades, scale=BIN_SIZE_SCALE) assert 2 == len(df) assert df.index[0] < df.index[1] assert (trades['price'].min() - BIN_SIZE_SCALE) < df.index[0] < trades['price'].max() assert (df.index[0] + BIN_SIZE_SCALE) >= df.index[1] assert (trades['price'].max() - BIN_SIZE_SCALE) < df.index[-1] < trades['price'].max() assert 1667.0 == df.index[-1] # bid assert 763.7 == df['ask'].iat[0] # ask assert 710.98 == df['bid_amount'].iat[0] assert 111 == df['bid'].iat[0] assert 52.7199999 == pytest.approx(df['delta'].iat[0]) # delta # assert 50.659 == df['bid'].iat[-1] # bid # assert 108.21 == df['ask'].iat[-1] # ask # assert 57.551 == df['delta'].iat[-1] # delta # # bidask def test_public_trades_testdata_sanity( candles, public_trades_list, public_trades_list_simple, populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades): assert 10999 == len(candles) assert 1000 == len(public_trades_list) assert 999 == len(populate_dataframe_with_trades_dataframe) assert 293532 == len(populate_dataframe_with_trades_trades) assert 7 == len(public_trades_list_simple) assert 5 == public_trades_list_simple.loc[ public_trades_list_simple['side'].str.contains( 'sell'), 'id'].count() assert 2 == public_trades_list_simple.loc[ public_trades_list_simple['side'].str.contains( 'buy'), 'id'].count() assert public_trades_list.columns.tolist() == [ 'timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost', 'date'] assert public_trades_list.columns.tolist() == [ 'timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost', 'date'] assert public_trades_list_simple.columns.tolist() == [ 'timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost', 'date'] assert populate_dataframe_with_trades_dataframe.columns.tolist() == [ 'date', 'open', 'high', 'low', 'close', 'volume'] assert populate_dataframe_with_trades_trades.columns.tolist() == [ 'timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost', 'date']