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fix tests: add comments

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Joe Schr
2024-02-26 11:23:09 +01:00
parent 5b637bc9fc
commit 6bdf6bed7b
1 changed files with 154 additions and 74 deletions
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228
tests/data/test_converter_public_trades.py
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@@ -61,11 +61,15 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
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,
@@ -73,28 +77,47 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
'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, pair='unitttest')
# Extract results from the first row of the DataFrame
results = df.iloc[0]
t = results['trades']
of = results['orderflow']
assert 0 != len(results) # 13 columns
# Assert basic properties of the results
assert 0 != len(results)
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()
# --- 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 19 == len(of)
assert [1.0, 0.0, -12.536, 12.536, 0.0,
12.536, 1.0] == of.iloc[0].values.tolist()
# 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']
@@ -120,19 +143,35 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_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()
# 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)
# --- 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': {
@@ -142,21 +181,33 @@ def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
'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']
# Populate the DataFrame with trades and order flow data
df = populate_dataframe_with_trades(config, dataframe, trades, pair='unitttest')
# --- 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'])
@@ -166,70 +217,99 @@ def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
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)
"""
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 0.14 == candles['vp'][1].values.tolist()[1][2] # delta
# 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
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
# Convert the trade list to a DataFrame
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()
# 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 (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]
# 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(