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ruff format: orderflow / public trades
This commit is contained in:
Joe Schr
@@ -11,49 +11,68 @@ from freqtrade.data.converter.trade_converter import trades_list_to_df
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BIN_SIZE_SCALE = 0.5
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def read_csv(filename, converter_columns: list = ['side', 'type']):
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return pd.read_csv(filename, skipinitialspace=True, infer_datetime_format=True, index_col=0,
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parse_dates=True, converters={col: str.strip for col in converter_columns})
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def read_csv(filename, converter_columns: list = ["side", "type"]):
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return pd.read_csv(
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filename,
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skipinitialspace=True,
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infer_datetime_format=True,
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index_col=0,
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parse_dates=True,
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converters={col: str.strip for col in converter_columns},
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)
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@pytest.fixture
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def populate_dataframe_with_trades_dataframe(testdatadir):
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return pd.read_feather(testdatadir / 'orderflow/populate_dataframe_with_trades_DF.feather')
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return pd.read_feather(testdatadir / "orderflow/populate_dataframe_with_trades_DF.feather")
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@pytest.fixture
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def populate_dataframe_with_trades_trades(testdatadir):
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return pd.read_feather(testdatadir / 'orderflow/populate_dataframe_with_trades_TRADES.feather')
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return pd.read_feather(testdatadir / "orderflow/populate_dataframe_with_trades_TRADES.feather")
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@pytest.fixture
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def candles(testdatadir):
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return pd.read_json(testdatadir / 'orderflow/candles.json').copy()
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return pd.read_json(testdatadir / "orderflow/candles.json").copy()
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@pytest.fixture
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def public_trades_list(testdatadir):
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return read_csv(testdatadir / 'orderflow/public_trades_list.csv').copy()
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return read_csv(testdatadir / "orderflow/public_trades_list.csv").copy()
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@pytest.fixture
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def public_trades_list_simple(testdatadir):
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return read_csv(testdatadir / 'orderflow/public_trades_list_simple_example.csv').copy()
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return read_csv(testdatadir / "orderflow/public_trades_list_simple_example.csv").copy()
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def test_public_trades_columns_before_change(
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populate_dataframe_with_trades_dataframe,
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populate_dataframe_with_trades_trades):
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populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
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):
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assert populate_dataframe_with_trades_dataframe.columns.tolist() == [
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'date', 'open', 'high', 'low', 'close', 'volume']
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"date",
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"open",
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"high",
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"low",
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"close",
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"volume",
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]
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assert populate_dataframe_with_trades_trades.columns.tolist() == [
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'timestamp', 'id', 'type', 'side', 'price',
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'amount', 'cost', 'date']
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"timestamp",
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"id",
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"type",
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"side",
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"price",
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"amount",
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"cost",
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"date",
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]
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def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
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populate_dataframe_with_trades_dataframe,
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populate_dataframe_with_trades_trades):
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populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
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):
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"""
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Tests the `populate_dataframe_with_trades` function's order flow calculation.
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@@ -64,24 +83,25 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
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dataframe = populate_dataframe_with_trades_dataframe.copy()
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trades = populate_dataframe_with_trades_trades.copy()
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# Convert the 'date' column to datetime format with milliseconds
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dataframe['date'] = pd.to_datetime(
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dataframe['date'], unit='ms')
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dataframe["date"] = pd.to_datetime(dataframe["date"], unit="ms")
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# Select the last rows and reset the index (optional, depends on usage)
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dataframe = dataframe.copy().tail().reset_index(drop=True)
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# Define the configuration for order flow calculation
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config = {'timeframe': '5m',
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'orderflow': {
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'scale': 0.005,
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'imbalance_volume': 0,
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'imbalance_ratio': 300,
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'stacked_imbalance_range': 3
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}}
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config = {
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"timeframe": "5m",
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"orderflow": {
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"scale": 0.005,
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"imbalance_volume": 0,
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"imbalance_ratio": 300,
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"stacked_imbalance_range": 3,
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},
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}
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# Apply the function to populate the data frame with order flow data
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df = populate_dataframe_with_trades(config, dataframe, trades)
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# Extract results from the first row of the DataFrame
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results = df.iloc[0]
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t = results['trades']
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of = results['orderflow']
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t = results["trades"]
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of = results["orderflow"]
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# Assert basic properties of the results
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assert 0 != len(results)
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@@ -98,7 +118,7 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
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assert [0.0, 1.0, 0.103, 0.0, 0.103, 0.103, 1.0] == of.iloc[-1].values.tolist()
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# Extract order flow from the last row of the DataFrame
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of = df.iloc[-1]['orderflow']
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of = df.iloc[-1]["orderflow"]
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# Assert number of order flow data points in the last row
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assert 19 == len(of) # Assert expected number of data points
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@@ -107,42 +127,49 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
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assert [1.0, 0.0, -12.536, 12.536, 0.0, 12.536, 1.0] == of.iloc[0].values.tolist()
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# Assert specific order flow values at the end of the last row
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assert [4.0, 3.0, -40.94800000000001, 59.18200000000001,
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18.233999999999998, 77.41600000000001, 7.0] == of.iloc[-1].values.tolist()
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assert [
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4.0,
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3.0,
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-40.94800000000001,
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59.18200000000001,
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18.233999999999998,
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77.41600000000001,
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7.0,
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] == of.iloc[-1].values.tolist()
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# --- Delta and Other Results ---
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# Assert delta value from the first row
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assert -50.519000000000005 == results['delta']
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assert -50.519000000000005 == results["delta"]
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# Assert min and max delta values from the first row
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assert -79.469 == results['min_delta']
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assert 17.298 == results['max_delta']
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assert -79.469 == results["min_delta"]
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assert 17.298 == results["max_delta"]
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# Assert that stacked imbalances are NaN (not applicable in this test)
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assert np.isnan(results['stacked_imbalances_bid'])
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assert np.isnan(results['stacked_imbalances_ask'])
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assert np.isnan(results["stacked_imbalances_bid"])
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assert np.isnan(results["stacked_imbalances_ask"])
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# Repeat assertions for the third from last row
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results = df.iloc[-2]
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assert -20.86200000000008 == results['delta']
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assert -54.55999999999999 == results['min_delta']
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assert 82.842 == results['max_delta']
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assert 234.99 == results['stacked_imbalances_bid']
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assert 234.96 == results['stacked_imbalances_ask']
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assert -20.86200000000008 == results["delta"]
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assert -54.55999999999999 == results["min_delta"]
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assert 82.842 == results["max_delta"]
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assert 234.99 == results["stacked_imbalances_bid"]
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assert 234.96 == results["stacked_imbalances_ask"]
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# Repeat assertions for the last row
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results = df.iloc[-1]
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assert -49.30200000000002 == results['delta']
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assert -70.222 == results['min_delta']
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assert 11.213000000000003 == results['max_delta']
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assert np.isnan(results['stacked_imbalances_bid'])
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assert np.isnan(results['stacked_imbalances_ask'])
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assert -49.30200000000002 == results["delta"]
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assert -70.222 == results["min_delta"]
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assert 11.213000000000003 == results["max_delta"]
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assert np.isnan(results["stacked_imbalances_bid"])
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assert np.isnan(results["stacked_imbalances_ask"])
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def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
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populate_dataframe_with_trades_dataframe,
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populate_dataframe_with_trades_trades):
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populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
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):
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"""
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Tests the `populate_dataframe_with_trades` function's handling of trades,
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ensuring correct integration of trades data into the generated DataFrame.
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@@ -155,7 +182,7 @@ def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
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# --- Data Preparation ---
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# Convert the 'date' column to datetime format with milliseconds
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dataframe['date'] = pd.to_datetime(dataframe['date'], unit='ms')
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dataframe["date"] = pd.to_datetime(dataframe["date"], unit="ms")
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# Select the final row of the DataFrame
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dataframe = dataframe.tail().reset_index(drop=True)
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@@ -165,19 +192,19 @@ def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
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trades.reset_index(inplace=True, drop=True) # Reset index for clarity
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# Assert the first trade ID to ensure filtering worked correctly
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assert trades['id'][0] == '313881442'
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assert trades["id"][0] == "313881442"
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# --- Configuration and Function Call ---
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# Define configuration for order flow calculation (used for context)
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config = {
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'timeframe': '5m',
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'orderflow': {
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'scale': 0.5,
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'imbalance_volume': 0,
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'imbalance_ratio': 300,
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'stacked_imbalance_range': 3
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}
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"timeframe": "5m",
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"orderflow": {
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"scale": 0.5,
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"imbalance_volume": 0,
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"imbalance_ratio": 300,
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"stacked_imbalance_range": 3,
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},
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}
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# Populate the DataFrame with trades and order flow data
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@@ -190,28 +217,38 @@ def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
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# Assert DataFrame structure
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assert list(df.columns) == [
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# ... (list of expected column names)
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'date', 'open', 'high', 'low',
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'close', 'volume', 'trades', 'orderflow',
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'bid', 'ask', 'delta', 'min_delta',
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'max_delta', 'total_trades',
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'stacked_imbalances_bid',
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'stacked_imbalances_ask'
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"date",
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"open",
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"high",
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"low",
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"close",
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"volume",
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"trades",
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"orderflow",
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"bid",
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"ask",
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"delta",
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"min_delta",
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"max_delta",
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"total_trades",
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"stacked_imbalances_bid",
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"stacked_imbalances_ask",
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]
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# Assert delta, bid, and ask values
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assert -50.519 == pytest.approx(row['delta'])
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assert 219.961 == row['bid']
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assert 169.442 == row['ask']
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assert -50.519 == pytest.approx(row["delta"])
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assert 219.961 == row["bid"]
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assert 169.442 == row["ask"]
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# Assert the number of trades
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assert 151 == len(row.trades)
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# Assert specific details of the first trade
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t = row['trades'].iloc[0]
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assert trades['id'][0] == t["id"]
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assert int(trades['timestamp'][0]) == int(t['timestamp'])
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assert 'sell' == t['side']
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assert '313881442' == t['id']
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assert 234.72 == t['price']
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t = row["trades"].iloc[0]
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assert trades["id"][0] == t["id"]
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assert int(trades["timestamp"][0]) == int(t["timestamp"])
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assert "sell" == t["side"]
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assert "313881442" == t["id"]
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assert 234.72 == t["price"]
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def test_public_trades_put_volume_profile_into_ohlcv_candles(public_trades_list_simple, candles):
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@@ -232,18 +269,19 @@ def test_public_trades_put_volume_profile_into_ohlcv_candles(public_trades_list_
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df = trades_to_volumeprofile_with_total_delta_bid_ask(df, scale=BIN_SIZE_SCALE)
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# Initialize the 'vp' column in the candles DataFrame with NaNs
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candles['vp'] = np.nan
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candles["vp"] = np.nan
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# Select the second candle (index 1) and attempt to assign the volume profile data
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# (as a DataFrame) to the 'vp' element.
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candles.loc[candles.index == 1, ['vp']] = candles.loc[candles.index == 1, [
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'vp']].applymap(lambda x: pd.DataFrame(df.to_dict()))
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candles.loc[candles.index == 1, ["vp"]] = candles.loc[candles.index == 1, ["vp"]].applymap(
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lambda x: pd.DataFrame(df.to_dict())
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)
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# Assert the delta value in the 'vp' element of the second candle
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assert 0.14 == candles['vp'][1].values.tolist()[1][2]
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assert 0.14 == candles["vp"][1].values.tolist()[1][2]
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# Alternative assertion using `.iat` accessor (assuming correct assignment logic)
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assert 0.14 == candles['vp'][1]['delta'].iat[1]
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assert 0.14 == candles["vp"][1]["delta"].iat[1]
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def test_public_trades_binned_big_sample_list(public_trades_list):
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@@ -262,8 +300,15 @@ def test_public_trades_binned_big_sample_list(public_trades_list):
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df = trades_to_volumeprofile_with_total_delta_bid_ask(trades, scale=BIN_SIZE_SCALE)
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# Assert that the DataFrame has the expected columns
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assert df.columns.tolist() == ['bid', 'ask', 'delta', 'bid_amount',
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'ask_amount', 'total_volume', 'total_trades']
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assert df.columns.tolist() == [
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"bid",
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"ask",
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"delta",
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"bid_amount",
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"ask_amount",
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"total_volume",
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"total_trades",
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]
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# Assert the number of rows in the DataFrame (expected 23 for this bin size)
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assert len(df) == 23
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@@ -271,22 +316,22 @@ def test_public_trades_binned_big_sample_list(public_trades_list):
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# Assert that the index values are in ascending order and spaced correctly
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assert all(df.index[i] < df.index[i + 1] for i in range(len(df) - 1))
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assert df.index[0] + BIN_SIZE_SCALE == df.index[1]
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assert (trades['price'].min() - BIN_SIZE_SCALE) < df.index[0] < trades['price'].max()
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assert (trades["price"].min() - BIN_SIZE_SCALE) < df.index[0] < trades["price"].max()
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assert (df.index[0] + BIN_SIZE_SCALE) >= df.index[1]
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assert (trades['price'].max() - BIN_SIZE_SCALE) < df.index[-1] < trades['price'].max()
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assert (trades["price"].max() - BIN_SIZE_SCALE) < df.index[-1] < trades["price"].max()
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# Assert specific values in the first and last rows of the DataFrame
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assert 32 == df['bid'].iloc[0] # bid price
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assert 197.512 == df['bid_amount'].iloc[0] # total bid amount
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assert 88.98 == df['ask_amount'].iloc[0] # total ask amount
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assert 26 == df['ask'].iloc[0] # ask price
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assert -108.532 == pytest.approx(df['delta'].iloc[0]) # delta (bid amount - ask amount)
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assert 32 == df["bid"].iloc[0] # bid price
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assert 197.512 == df["bid_amount"].iloc[0] # total bid amount
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assert 88.98 == df["ask_amount"].iloc[0] # total ask amount
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assert 26 == df["ask"].iloc[0] # ask price
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assert -108.532 == pytest.approx(df["delta"].iloc[0]) # delta (bid amount - ask amount)
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assert 3 == df['bid'].iloc[-1] # bid price
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assert 50.659 == df['bid_amount'].iloc[-1] # total bid amount
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assert 108.21 == df['ask_amount'].iloc[-1] # total ask amount
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assert 44 == df['ask'].iloc[-1] # ask price
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assert 57.551 == df['delta'].iloc[-1] # delta (bid amount - ask amount)
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assert 3 == df["bid"].iloc[-1] # bid price
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assert 50.659 == df["bid_amount"].iloc[-1] # total bid amount
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assert 108.21 == df["ask_amount"].iloc[-1] # total ask amount
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assert 44 == df["ask"].iloc[-1] # ask price
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assert 57.551 == df["delta"].iloc[-1] # delta (bid amount - ask amount)
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# Repeat the process with a larger bin size
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BIN_SIZE_SCALE = 1
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@@ -299,43 +344,89 @@ def test_public_trades_binned_big_sample_list(public_trades_list):
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# Repeat similar assertions for index ordering and spacing
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assert all(df.index[i] < df.index[i + 1] for i in range(len(df) - 1))
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assert (trades['price'].min() - BIN_SIZE_SCALE) < df.index[0] < trades['price'].max()
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assert (trades["price"].min() - BIN_SIZE_SCALE) < df.index[0] < trades["price"].max()
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assert (df.index[0] + BIN_SIZE_SCALE) >= df.index[1]
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assert (trades['price'].max() - BIN_SIZE_SCALE) < df.index[-1] < trades['price'].max()
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assert (trades["price"].max() - BIN_SIZE_SCALE) < df.index[-1] < trades["price"].max()
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# Assert the value in the last row of the DataFrame with the larger bin size
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assert 1667.0 == df.index[-1]
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assert 710.98 == df['bid_amount'].iat[0]
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assert 111 == df['bid'].iat[0]
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assert 52.7199999 == pytest.approx(df['delta'].iat[0]) # delta
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assert 710.98 == df["bid_amount"].iat[0]
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assert 111 == df["bid"].iat[0]
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assert 52.7199999 == pytest.approx(df["delta"].iat[0]) # delta
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def test_public_trades_testdata_sanity(
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candles,
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public_trades_list,
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||||
public_trades_list_simple,
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||||
populate_dataframe_with_trades_dataframe,
|
||||
populate_dataframe_with_trades_trades):
|
||||
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 (
|
||||
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']
|
||||
"timestamp",
|
||||
"id",
|
||||
"type",
|
||||
"side",
|
||||
"price",
|
||||
"amount",
|
||||
"cost",
|
||||
"date",
|
||||
]
|
||||
|
||||
assert public_trades_list.columns.tolist() == [
|
||||
'timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost', 'date']
|
||||
"timestamp",
|
||||
"id",
|
||||
"type",
|
||||
"side",
|
||||
"price",
|
||||
"amount",
|
||||
"cost",
|
||||
"date",
|
||||
]
|
||||
assert public_trades_list_simple.columns.tolist() == [
|
||||
'timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost', 'date']
|
||||
"timestamp",
|
||||
"id",
|
||||
"type",
|
||||
"side",
|
||||
"price",
|
||||
"amount",
|
||||
"cost",
|
||||
"date",
|
||||
]
|
||||
assert populate_dataframe_with_trades_dataframe.columns.tolist() == [
|
||||
'date', 'open', 'high', 'low', 'close', 'volume']
|
||||
"date",
|
||||
"open",
|
||||
"high",
|
||||
"low",
|
||||
"close",
|
||||
"volume",
|
||||
]
|
||||
assert populate_dataframe_with_trades_trades.columns.tolist() == [
|
||||
'timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost', 'date']
|
||||
"timestamp",
|
||||
"id",
|
||||
"type",
|
||||
"side",
|
||||
"price",
|
||||
"amount",
|
||||
"cost",
|
||||
"date",
|
||||
]
|
||||
|
||||
@@ -66,7 +66,7 @@ def test_historic_trades(mocker, default_conf, trades_history_df):
|
||||
historymock = MagicMock(return_value=trades_history_df)
|
||||
mocker.patch(
|
||||
"freqtrade.data.history.datahandlers.featherdatahandler.FeatherDataHandler._trades_load",
|
||||
historymock
|
||||
historymock,
|
||||
)
|
||||
|
||||
dp = DataProvider(default_conf, None)
|
||||
@@ -82,8 +82,8 @@ def test_historic_trades(mocker, default_conf, trades_history_df):
|
||||
assert len(data) == 0
|
||||
|
||||
# Switch to backtest mode
|
||||
default_conf['runmode'] = RunMode.BACKTEST
|
||||
default_conf['dataformat_trades'] = 'feather'
|
||||
default_conf["runmode"] = RunMode.BACKTEST
|
||||
default_conf["dataformat_trades"] = "feather"
|
||||
exchange = get_patched_exchange(mocker, default_conf)
|
||||
dp = DataProvider(default_conf, exchange)
|
||||
data = dp.trades("UNITTEST/BTC", "5m")
|
||||
@@ -91,7 +91,7 @@ def test_historic_trades(mocker, default_conf, trades_history_df):
|
||||
assert len(data) == len(trades_history_df)
|
||||
|
||||
# Random other runmode
|
||||
default_conf['runmode'] = RunMode.UTIL_EXCHANGE
|
||||
default_conf["runmode"] = RunMode.UTIL_EXCHANGE
|
||||
dp = DataProvider(default_conf, None)
|
||||
data = dp.trades("UNITTEST/BTC", "5m")
|
||||
assert isinstance(data, DataFrame)
|
||||
@@ -311,7 +311,7 @@ def test_refresh(mocker, default_conf):
|
||||
# Test with public trades
|
||||
refresh_mock.reset_mock()
|
||||
refresh_mock.reset_mock()
|
||||
default_conf['exchange']['use_public_trades'] = True
|
||||
default_conf["exchange"]["use_public_trades"] = True
|
||||
dp.refresh(pairs, pairs_non_trad)
|
||||
assert mock_refresh_trades.call_count == 1
|
||||
assert refresh_mock.call_count == 1
|
||||
|
||||
@@ -674,8 +674,9 @@ def test_download_trades_history(
|
||||
mocker.patch(f"{EXMS}.get_historic_trades", MagicMock(side_effect=ValueError))
|
||||
caplog.clear()
|
||||
|
||||
assert not _download_trades_history(data_handler=data_handler, exchange=exchange,
|
||||
pair='ETH/BTC', trading_mode=TradingMode.SPOT)
|
||||
assert not _download_trades_history(
|
||||
data_handler=data_handler, exchange=exchange, pair="ETH/BTC", trading_mode=TradingMode.SPOT
|
||||
)
|
||||
assert log_has_re('Failed to download and store historic trades for pair: "ETH/BTC".*', caplog)
|
||||
|
||||
file2 = tmp_path / "XRP_ETH-trades.json.gz"
|
||||
|
||||
Reference in New Issue
Block a user