mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-15 20:53:58 +00:00
347 lines
14 KiB
Python
347 lines
14 KiB
Python
|
|
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']
|