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freqtrade_origin/tests/data/test_converter_public_trades.py

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import logging
from pathlib import Path
import arrow
import numpy as np
import pandas as pd
import pytest
from pandas import DataFrame
from freqtrade.configuration import Configuration
from freqtrade.constants import DEFAULT_ORDERFLOW_COLUMNS
from freqtrade.data.converter import (
populate_dataframe_with_trades, public_trades_to_dataframe)
from freqtrade.data.converter.converter import (
trades_to_volumeprofile_with_total_delta_bid_ask)
from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.exchange.exchange import timeframe_to_minutes
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 conjuresetup():
public_trades_list = public_trades_list()
print(public_trades_list.columns.tolist())
public_trades_list_simple = public_trades_list_simple()
print(public_trades_list_simple.columns.tolist())
print(public_trades_list_simple.loc[:, [
'timestamp', 'id', 'price', 'side', 'amount']])
public_trades_list_simple_results = public_trades_list_simple_results()
print(public_trades_list_simple_results.columns.tolist())
public_trades_list_simple_bidask = public_trades_list_simple_bidask()
print(public_trades_list_simple_bidask.columns.tolist())
print(public_trades_list_simple_bidask)
print(public_trades_list_simple_results)
# conjuresetup() # never called except in REPL
# /conjuresetup
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')
result = df.iloc[0]
assert result.index.values.tolist() == ['date', 'open', 'high', 'low', 'close', 'volume', 'trades', 'orderflow',
'bid', 'ask', 'delta', 'min_delta', 'max_delta', 'total_trades', 'stacked_imbalances_bid', 'stacked_imbalances_ask']
assert -50.519000000000005 == result['delta']
assert 219.961 == result['bid']
assert 169.442 == result['ask']
assert 151 == len(result.trades)
t = result['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 = public_trades_to_dataframe(
public_trades_list_simple, '1m', 'doesntmatter', fill_missing=False, drop_incomplete=False)
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 = public_trades_to_dataframe(
public_trades_list, '1m', 'doesntmatter',
fill_missing=False, drop_incomplete=False)
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 -108.53200000000001 == 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 = public_trades_to_dataframe(
public_trades_list, '1m', 'doesntmatter',
fill_missing=False, drop_incomplete=False)
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 52.71999999999997 == 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 do_plot(pair, data, trades, plot_config=None):
import plotly.offline as pyo
from freqtrade.plot.plotting import generate_candlestick_graph
# Filter trades to one pair
trades_red = trades # .loc[trades['pair'] == pair].copy()
# Limit graph period to your BT timerange
data_red = data # data['2021-04-01':'2021-04-20']
# plotconf = strategy.plot_config
plotconf = plot_config
# Generate candlestick graph
graph = generate_candlestick_graph(pair=pair,
data=data_red,
trades=trades_red,
plot_config=plotconf
)
pyo.plot(graph, output_type="file", show_link=False,
filename="tests/data/test_converter_public_trades.html")
# need to be at last to see if some test changed the testdata
# always need to use .copy() to avoid changing the testdata
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']
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