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 def populate_dataframe_with_trades_dataframe(testdatadir): return pd.read_feather(testdatadir / 'orderflow/populate_dataframe_with_trades_DF.feather') @pytest.fixture def populate_dataframe_with_trades_trades(testdatadir): return pd.read_feather(testdatadir / 'orderflow/populate_dataframe_with_trades_TRADES.feather') @pytest.fixture def candles(testdatadir): return pd.read_json(testdatadir / 'orderflow/candles.json').copy() @pytest.fixture def public_trades_list(testdatadir): return read_csv(testdatadir / 'orderflow/public_trades_list.csv').copy() @pytest.fixture def public_trades_list_simple(testdatadir): return read_csv(testdatadir / 'orderflow/public_trades_list_simple_example.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): """ Tests the `populate_dataframe_with_trades` function's order flow calculation. This test checks the generated data frame and order flow for specific properties based on the provided configuration and sample data. """ # Create copies of the input data to avoid modifying the originals dataframe = populate_dataframe_with_trades_dataframe.copy() trades = populate_dataframe_with_trades_trades.copy() # Convert the 'date' column to datetime format with milliseconds dataframe['date'] = pd.to_datetime( dataframe['date'], unit='ms') # Select the last rows and reset the index (optional, depends on usage) dataframe = dataframe.copy().tail().reset_index(drop=True) # Define the configuration for order flow calculation config = {'timeframe': '5m', 'orderflow': { 'scale': 0.005, 'imbalance_volume': 0, 'imbalance_ratio': 300, 'stacked_imbalance_range': 3 }} # Apply the function to populate the data frame with order flow data df = populate_dataframe_with_trades(config, dataframe, trades) # Extract results from the first row of the DataFrame results = df.iloc[0] t = results['trades'] of = results['orderflow'] # Assert basic properties of the results assert 0 != len(results) assert 151 == len(t) # --- Order Flow Analysis --- # Assert number of order flow data points assert 23 == len(of) # Assert expected number of data points # Assert specific order flow values at the beginning of the DataFrame assert [0.0, 1.0, 4.999, 0.0, 4.999, 4.999, 1.0] == of.iloc[0].values.tolist() # Assert specific order flow values at the end of the DataFrame (excluding last row) assert [0.0, 1.0, 0.103, 0.0, 0.103, 0.103, 1.0] == of.iloc[-1].values.tolist() # Extract order flow from the last row of the DataFrame of = df.iloc[-1]['orderflow'] # Assert number of order flow data points in the last row assert 19 == len(of) # Assert expected number of data points # Assert specific order flow values at the beginning of the last row assert [1.0, 0.0, -12.536, 12.536, 0.0, 12.536, 1.0] == of.iloc[0].values.tolist() # Assert specific order flow values at the end of the last row assert [4.0, 3.0, -40.94800000000001, 59.18200000000001, 18.233999999999998, 77.41600000000001, 7.0] == of.iloc[-1].values.tolist() # --- Delta and Other Results --- # Assert delta value from the first row assert -50.519000000000005 == results['delta'] # Assert min and max delta values from the first row assert -79.469 == results['min_delta'] assert 17.298 == results['max_delta'] # Assert that stacked imbalances are NaN (not applicable in this test) assert np.isnan(results['stacked_imbalances_bid']) assert np.isnan(results['stacked_imbalances_ask']) # Repeat assertions for the third from last row results = df.iloc[-2] assert -20.86200000000008 == results['delta'] assert -54.55999999999999 == results['min_delta'] assert 82.842 == results['max_delta'] assert 234.99 == results['stacked_imbalances_bid'] assert 234.96 == results['stacked_imbalances_ask'] # Repeat assertions for the last row 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): """ Tests the `populate_dataframe_with_trades` function's handling of trades, ensuring correct integration of trades data into the generated DataFrame. """ # Create copies of the input data to avoid modifying the originals dataframe = populate_dataframe_with_trades_dataframe.copy() trades = populate_dataframe_with_trades_trades.copy() # --- Data Preparation --- # Convert the 'date' column to datetime format with milliseconds dataframe['date'] = pd.to_datetime(dataframe['date'], unit='ms') # Select the final row of the DataFrame dataframe = dataframe.tail().reset_index(drop=True) # Filter trades to those occurring after or at the same time as the first DataFrame date trades = trades.loc[trades.date >= dataframe.date[0]] trades.reset_index(inplace=True, drop=True) # Reset index for clarity # Assert the first trade ID to ensure filtering worked correctly assert trades['id'][0] == '313881442' # --- Configuration and Function Call --- # Define configuration for order flow calculation (used for context) config = { 'timeframe': '5m', 'orderflow': { 'scale': 0.5, 'imbalance_volume': 0, 'imbalance_ratio': 300, 'stacked_imbalance_range': 3 } } # Populate the DataFrame with trades and order flow data df = populate_dataframe_with_trades(config, dataframe, trades) # --- DataFrame and Trade Data Validation --- row = df.iloc[0] # Extract the first row for assertions # Assert DataFrame structure assert list(df.columns) == [ # ... (list of expected column names) 'date', 'open', 'high', 'low', 'close', 'volume', 'trades', 'orderflow', 'bid', 'ask', 'delta', 'min_delta', 'max_delta', 'total_trades', 'stacked_imbalances_bid', 'stacked_imbalances_ask' ] # Assert delta, bid, and ask values assert -50.519 == pytest.approx(row['delta']) assert 219.961 == row['bid'] assert 169.442 == row['ask'] # Assert the number of trades assert 151 == len(row.trades) # Assert specific details of the first trade 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): """ Tests the integration of volume profile data into OHLCV candles. This test verifies that the `trades_to_volumeprofile_with_total_delta_bid_ask` function correctly calculates the volume profile and that it correctly assigns the delta value from the volume profile to the corresponding candle in the `candles` DataFrame. """ # Convert the trade list to a DataFrame df = trades_list_to_df(public_trades_list_simple[DEFAULT_TRADES_COLUMNS].values.tolist()) # Generate the volume profile with the specified bin size df = trades_to_volumeprofile_with_total_delta_bid_ask(df, scale=BIN_SIZE_SCALE) # Initialize the 'vp' column in the candles DataFrame with NaNs candles['vp'] = np.nan # Select the second candle (index 1) and attempt to assign the volume profile data # (as a DataFrame) to the 'vp' element. candles.loc[candles.index == 1, ['vp']] = candles.loc[candles.index == 1, [ 'vp']].applymap(lambda x: pd.DataFrame(df.to_dict())) # Assert the delta value in the 'vp' element of the second candle assert 0.14 == candles['vp'][1].values.tolist()[1][2] # Alternative assertion using `.iat` accessor (assuming correct assignment logic) assert 0.14 == candles['vp'][1]['delta'].iat[1] def test_public_trades_binned_big_sample_list(public_trades_list): """ Tests the `trades_to_volumeprofile_with_total_delta_bid_ask` function with different bin sizes and verifies the generated DataFrame's structure and values. """ # Define the bin size for the first test BIN_SIZE_SCALE = 0.05 # Convert the trade list to a DataFrame trades = trades_list_to_df(public_trades_list[DEFAULT_TRADES_COLUMNS].values.tolist()) # Generate the volume profile with the specified bin size df = trades_to_volumeprofile_with_total_delta_bid_ask(trades, scale=BIN_SIZE_SCALE) # Assert that the DataFrame has the expected columns assert df.columns.tolist() == ['bid', 'ask', 'delta', 'bid_amount', 'ask_amount', 'total_volume', 'total_trades'] # Assert the number of rows in the DataFrame (expected 23 for this bin size) assert len(df) == 23 # Assert that the index values are in ascending order and spaced correctly assert all(df.index[i] < df.index[i + 1] for i in range(len(df) - 1)) 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 specific values in the first and last rows of the DataFrame assert 32 == df['bid'].iloc[0] # bid price assert 197.512 == df['bid_amount'].iloc[0] # total bid amount assert 88.98 == df['ask_amount'].iloc[0] # total ask amount assert 26 == df['ask'].iloc[0] # ask price assert -108.532 == pytest.approx(df['delta'].iloc[0]) # delta (bid amount - ask amount) assert 3 == df['bid'].iloc[-1] # bid price assert 50.659 == df['bid_amount'].iloc[-1] # total bid amount assert 108.21 == df['ask_amount'].iloc[-1] # total ask amount assert 44 == df['ask'].iloc[-1] # ask price assert 57.551 == df['delta'].iloc[-1] # delta (bid amount - ask amount) # Repeat the process with a larger bin size BIN_SIZE_SCALE = 1 # Generate the volume profile with the larger bin size df = trades_to_volumeprofile_with_total_delta_bid_ask(trades, scale=BIN_SIZE_SCALE) # Assert the number of rows in the DataFrame (expected 2 for this bin size) assert len(df) == 2 # Repeat similar assertions for index ordering and spacing assert all(df.index[i] < df.index[i + 1] for i in range(len(df) - 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 the value in the last row of the DataFrame with the larger bin size assert 1667.0 == df.index[-1] assert 710.98 == df['bid_amount'].iat[0] assert 111 == df['bid'].iat[0] assert 52.7199999 == pytest.approx(df['delta'].iat[0]) # delta 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']