mirror of
https://github.com/freqtrade/freqtrade.git
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407 lines
17 KiB
Python
407 lines
17 KiB
Python
import numpy as np
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import pandas as pd
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import pytest
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.enums import CandleType
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from freqtrade.resolvers.strategy_resolver import StrategyResolver
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from freqtrade.strategy import merge_informative_pair, stoploss_from_absolute, stoploss_from_open
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from tests.conftest import generate_test_data, get_patched_exchange
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def test_merge_informative_pair():
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data = generate_test_data("15m", 40)
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informative = generate_test_data("1h", 40)
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cols_inf = list(informative.columns)
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result = merge_informative_pair(data, informative, "15m", "1h", ffill=True)
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assert isinstance(result, pd.DataFrame)
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assert list(informative.columns) == cols_inf
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assert len(result) == len(data)
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assert "date" in result.columns
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assert result["date"].equals(data["date"])
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assert "date_1h" in result.columns
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assert "open" in result.columns
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assert "open_1h" in result.columns
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assert result["open"].equals(data["open"])
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assert "close" in result.columns
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assert "close_1h" in result.columns
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assert result["close"].equals(data["close"])
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assert "volume" in result.columns
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assert "volume_1h" in result.columns
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assert result["volume"].equals(data["volume"])
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# First 3 rows are empty
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assert result.iloc[0]["date_1h"] is pd.NaT
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assert result.iloc[1]["date_1h"] is pd.NaT
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assert result.iloc[2]["date_1h"] is pd.NaT
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# Next 4 rows contain the starting date (0:00)
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assert result.iloc[3]["date_1h"] == result.iloc[0]["date"]
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assert result.iloc[4]["date_1h"] == result.iloc[0]["date"]
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assert result.iloc[5]["date_1h"] == result.iloc[0]["date"]
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assert result.iloc[6]["date_1h"] == result.iloc[0]["date"]
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# Next 4 rows contain the next Hourly date original date row 4
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assert result.iloc[7]["date_1h"] == result.iloc[4]["date"]
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assert result.iloc[8]["date_1h"] == result.iloc[4]["date"]
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informative = generate_test_data("1h", 40)
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result = merge_informative_pair(data, informative, "15m", "1h", ffill=False)
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# First 3 rows are empty
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assert result.iloc[0]["date_1h"] is pd.NaT
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assert result.iloc[1]["date_1h"] is pd.NaT
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assert result.iloc[2]["date_1h"] is pd.NaT
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# Next 4 rows contain the starting date (0:00)
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assert result.iloc[3]["date_1h"] == result.iloc[0]["date"]
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assert result.iloc[4]["date_1h"] is pd.NaT
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assert result.iloc[5]["date_1h"] is pd.NaT
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assert result.iloc[6]["date_1h"] is pd.NaT
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# Next 4 rows contain the next Hourly date original date row 4
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assert result.iloc[7]["date_1h"] == result.iloc[4]["date"]
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assert result.iloc[8]["date_1h"] is pd.NaT
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def test_merge_informative_pair_weekly():
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# Covers roughly 2 months - until 2023-01-10
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data = generate_test_data("1h", 1040, "2022-11-28")
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informative = generate_test_data("1w", 40, "2022-11-01")
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informative["day"] = informative["date"].dt.day_name()
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result = merge_informative_pair(data, informative, "1h", "1w", ffill=True)
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assert isinstance(result, pd.DataFrame)
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# 2022-12-24 is a Saturday
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candle1 = result.loc[(result["date"] == "2022-12-24T22:00:00.000Z")]
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assert candle1.iloc[0]["date"] == pd.Timestamp("2022-12-24T22:00:00.000Z")
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assert candle1.iloc[0]["date_1w"] == pd.Timestamp("2022-12-12T00:00:00.000Z")
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candle2 = result.loc[(result["date"] == "2022-12-24T23:00:00.000Z")]
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assert candle2.iloc[0]["date"] == pd.Timestamp("2022-12-24T23:00:00.000Z")
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assert candle2.iloc[0]["date_1w"] == pd.Timestamp("2022-12-12T00:00:00.000Z")
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# 2022-12-25 is a Sunday
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candle3 = result.loc[(result["date"] == "2022-12-25T22:00:00.000Z")]
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assert candle3.iloc[0]["date"] == pd.Timestamp("2022-12-25T22:00:00.000Z")
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# Still old candle
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assert candle3.iloc[0]["date_1w"] == pd.Timestamp("2022-12-12T00:00:00.000Z")
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candle4 = result.loc[(result["date"] == "2022-12-25T23:00:00.000Z")]
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assert candle4.iloc[0]["date"] == pd.Timestamp("2022-12-25T23:00:00.000Z")
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assert candle4.iloc[0]["date_1w"] == pd.Timestamp("2022-12-19T00:00:00.000Z")
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def test_merge_informative_pair_monthly():
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# Covers roughly 2 months - until 2023-01-10
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data = generate_test_data("1h", 1040, "2022-11-28")
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informative = generate_test_data("1M", 40, "2022-01-01")
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result = merge_informative_pair(data, informative, "1h", "1M", ffill=True)
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assert isinstance(result, pd.DataFrame)
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candle1 = result.loc[(result["date"] == "2022-12-31T22:00:00.000Z")]
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assert candle1.iloc[0]["date"] == pd.Timestamp("2022-12-31T22:00:00.000Z")
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assert candle1.iloc[0]["date_1M"] == pd.Timestamp("2022-11-01T00:00:00.000Z")
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candle2 = result.loc[(result["date"] == "2022-12-31T23:00:00.000Z")]
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assert candle2.iloc[0]["date"] == pd.Timestamp("2022-12-31T23:00:00.000Z")
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assert candle2.iloc[0]["date_1M"] == pd.Timestamp("2022-12-01T00:00:00.000Z")
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# Candle is empty, as the start-date did fail.
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candle3 = result.loc[(result["date"] == "2022-11-30T22:00:00.000Z")]
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assert candle3.iloc[0]["date"] == pd.Timestamp("2022-11-30T22:00:00.000Z")
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assert candle3.iloc[0]["date_1M"] is pd.NaT
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# First candle with 1M data merged.
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candle4 = result.loc[(result["date"] == "2022-11-30T23:00:00.000Z")]
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assert candle4.iloc[0]["date"] == pd.Timestamp("2022-11-30T23:00:00.000Z")
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assert candle4.iloc[0]["date_1M"] == pd.Timestamp("2022-11-01T00:00:00.000Z")
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def test_merge_informative_pair_same():
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data = generate_test_data("15m", 40)
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informative = generate_test_data("15m", 40)
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result = merge_informative_pair(data, informative, "15m", "15m", ffill=True)
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assert isinstance(result, pd.DataFrame)
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assert len(result) == len(data)
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assert "date" in result.columns
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assert result["date"].equals(data["date"])
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assert "date_15m" in result.columns
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assert "open" in result.columns
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assert "open_15m" in result.columns
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assert result["open"].equals(data["open"])
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assert "close" in result.columns
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assert "close_15m" in result.columns
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assert result["close"].equals(data["close"])
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assert "volume" in result.columns
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assert "volume_15m" in result.columns
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assert result["volume"].equals(data["volume"])
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# Dates match 1:1
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assert result["date_15m"].equals(result["date"])
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def test_merge_informative_pair_lower():
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data = generate_test_data("1h", 40)
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informative = generate_test_data("15m", 40)
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with pytest.raises(ValueError, match=r"Tried to merge a faster timeframe .*"):
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merge_informative_pair(data, informative, "1h", "15m", ffill=True)
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def test_merge_informative_pair_empty():
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data = generate_test_data("1h", 40)
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informative = pd.DataFrame(columns=data.columns)
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result = merge_informative_pair(data, informative, "1h", "2h", ffill=True)
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assert result["date"].equals(data["date"])
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assert list(result.columns) == [
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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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"date_2h",
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"open_2h",
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"high_2h",
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"low_2h",
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"close_2h",
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"volume_2h",
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]
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# We merge an empty dataframe, so all values should be NaN
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for col in ["date_2h", "open_2h", "high_2h", "low_2h", "close_2h", "volume_2h"]:
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assert result[col].isnull().all()
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def test_merge_informative_pair_suffix():
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data = generate_test_data("15m", 20)
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informative = generate_test_data("1h", 20)
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result = merge_informative_pair(
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data, informative, "15m", "1h", append_timeframe=False, suffix="suf"
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)
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assert "date" in result.columns
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assert result["date"].equals(data["date"])
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assert "date_suf" in result.columns
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assert "open_suf" in result.columns
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assert "open_1h" not in result.columns
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assert list(result.columns) == [
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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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"date_suf",
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"open_suf",
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"high_suf",
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"low_suf",
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"close_suf",
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"volume_suf",
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]
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def test_merge_informative_pair_suffix_append_timeframe():
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data = generate_test_data("15m", 20)
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informative = generate_test_data("1h", 20)
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with pytest.raises(ValueError, match=r"You can not specify `append_timeframe` .*"):
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merge_informative_pair(data, informative, "15m", "1h", suffix="suf")
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@pytest.mark.parametrize(
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"side,profitrange",
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[
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# profit range for long is [-1, inf] while for shorts is [-inf, 1]
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("long", [-0.99, 2, 30]),
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("short", [-2.0, 0.99, 30]),
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],
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)
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def test_stoploss_from_open(side, profitrange):
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open_price_ranges = [
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[0.01, 1.00, 30],
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[1, 100, 30],
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[100, 10000, 30],
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]
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for open_range in open_price_ranges:
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for open_price in np.linspace(*open_range):
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for desired_stop in np.linspace(-0.50, 0.50, 30):
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if side == "long":
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# -1 is not a valid current_profit, should return 1
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assert stoploss_from_open(desired_stop, -1) == 1
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else:
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# 1 is not a valid current_profit for shorts, should return 1
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assert stoploss_from_open(desired_stop, 1, True) == 1
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for current_profit in np.linspace(*profitrange):
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if side == "long":
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current_price = open_price * (1 + current_profit)
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expected_stop_price = open_price * (1 + desired_stop)
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stoploss = stoploss_from_open(desired_stop, current_profit)
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stop_price = current_price * (1 - stoploss)
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else:
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current_price = open_price * (1 - current_profit)
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expected_stop_price = open_price * (1 - desired_stop)
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stoploss = stoploss_from_open(desired_stop, current_profit, True)
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stop_price = current_price * (1 + stoploss)
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assert stoploss >= 0
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# Technically the formula can yield values greater than 1 for shorts
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# even though it doesn't make sense because the position would be liquidated
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if side == "long":
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assert stoploss <= 1
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# there is no correct answer if the expected stop price is above
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# the current price
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if (side == "long" and expected_stop_price > current_price) or (
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side == "short" and expected_stop_price < current_price
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):
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assert stoploss == 0
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else:
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assert pytest.approx(stop_price) == expected_stop_price
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@pytest.mark.parametrize(
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"side,rel_stop,curr_profit,leverage,expected",
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[
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# profit range for long is [-1, inf] while for shorts is [-inf, 1]
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("long", 0, -1, 1, 1),
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("long", 0, 0.1, 1, 0.09090909),
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("long", -0.1, 0.1, 1, 0.18181818),
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("long", 0.1, 0.2, 1, 0.08333333),
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("long", 0.1, 0.5, 1, 0.266666666),
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("long", 0.1, 5, 1, 0.816666666), # 500% profit, set stoploss to 10% above open price
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("long", 0, 5, 10, 3.3333333), # 500% profit, set stoploss break even
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("long", 0.1, 5, 10, 3.26666666), # 500% profit, set stoploss to 10% above open price
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("long", -0.1, 5, 10, 3.3999999), # 500% profit, set stoploss to 10% belowopen price
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("short", 0, 0.1, 1, 0.1111111),
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("short", -0.1, 0.1, 1, 0.2222222),
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("short", 0.1, 0.2, 1, 0.125),
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("short", 0.1, 1, 1, 1),
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("short", -0.01, 5, 10, 10.01999999), # 500% profit at 10x
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],
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)
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def test_stoploss_from_open_leverage(side, rel_stop, curr_profit, leverage, expected):
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stoploss = stoploss_from_open(rel_stop, curr_profit, side == "short", leverage)
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assert pytest.approx(stoploss) == expected
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open_rate = 100
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if stoploss != 1:
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if side == "long":
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current_rate = open_rate * (1 + curr_profit / leverage)
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stop = current_rate * (1 - stoploss / leverage)
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assert pytest.approx(stop) == open_rate * (1 + rel_stop / leverage)
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else:
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current_rate = open_rate * (1 - curr_profit / leverage)
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stop = current_rate * (1 + stoploss / leverage)
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assert pytest.approx(stop) == open_rate * (1 - rel_stop / leverage)
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def test_stoploss_from_absolute():
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assert pytest.approx(stoploss_from_absolute(90, 100)) == 1 - (90 / 100)
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assert pytest.approx(stoploss_from_absolute(90, 100)) == 0.1
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assert pytest.approx(stoploss_from_absolute(95, 100)) == 0.05
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assert pytest.approx(stoploss_from_absolute(100, 100)) == 0
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assert pytest.approx(stoploss_from_absolute(110, 100)) == 0
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assert pytest.approx(stoploss_from_absolute(100, 0)) == 1
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assert pytest.approx(stoploss_from_absolute(0, 100)) == 1
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assert pytest.approx(stoploss_from_absolute(0, 100, False, leverage=5)) == 5
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assert pytest.approx(stoploss_from_absolute(90, 100, True)) == 0
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assert pytest.approx(stoploss_from_absolute(100, 100, True)) == 0
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assert pytest.approx(stoploss_from_absolute(110, 100, True)) == -(1 - (110 / 100))
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assert pytest.approx(stoploss_from_absolute(110, 100, True)) == 0.1
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assert pytest.approx(stoploss_from_absolute(105, 100, True)) == 0.05
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assert pytest.approx(stoploss_from_absolute(105, 100, True, 5)) == 0.05 * 5
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assert pytest.approx(stoploss_from_absolute(100, 0, True)) == 1
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assert pytest.approx(stoploss_from_absolute(0, 100, True)) == 0
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assert pytest.approx(stoploss_from_absolute(100, 1, is_short=True)) == 1
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assert pytest.approx(stoploss_from_absolute(100, 1, is_short=True, leverage=5)) == 5
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@pytest.mark.parametrize("trading_mode", ["futures", "spot"])
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def test_informative_decorator(mocker, default_conf_usdt, trading_mode):
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candle_def = CandleType.get_default(trading_mode)
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default_conf_usdt["candle_type_def"] = candle_def
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test_data_5m = generate_test_data("5m", 40)
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test_data_30m = generate_test_data("30m", 40)
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test_data_1h = generate_test_data("1h", 40)
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data = {
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("XRP/USDT", "5m", candle_def): test_data_5m,
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("XRP/USDT", "30m", candle_def): test_data_30m,
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("XRP/USDT", "1h", candle_def): test_data_1h,
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("XRP/BTC", "1h", candle_def): test_data_1h, # from {base}/BTC
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("LTC/USDT", "5m", candle_def): test_data_5m,
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("LTC/USDT", "30m", candle_def): test_data_30m,
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("LTC/USDT", "1h", candle_def): test_data_1h,
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("LTC/BTC", "1h", candle_def): test_data_1h, # from {base}/BTC
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("NEO/USDT", "30m", candle_def): test_data_30m,
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("NEO/USDT", "5m", CandleType.SPOT): test_data_5m, # Explicit request with '' as candletype
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("NEO/USDT", "15m", candle_def): test_data_5m, # Explicit request with '' as candletype
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("NEO/USDT", "1h", candle_def): test_data_1h,
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("ETH/USDT", "1h", candle_def): test_data_1h,
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("ETH/USDT", "30m", candle_def): test_data_30m,
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("ETH/BTC", "1h", CandleType.SPOT): test_data_1h, # Explicitly selected as spot
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}
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default_conf_usdt["strategy"] = "InformativeDecoratorTest"
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strategy = StrategyResolver.load_strategy(default_conf_usdt)
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exchange = get_patched_exchange(mocker, default_conf_usdt)
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strategy.dp = DataProvider({}, exchange, None)
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mocker.patch.object(
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strategy.dp, "current_whitelist", return_value=["XRP/USDT", "LTC/USDT", "NEO/USDT"]
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)
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assert len(strategy._ft_informative) == 7 # Equal to number of decorators used
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informative_pairs = [
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("XRP/USDT", "1h", candle_def),
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("XRP/BTC", "1h", candle_def),
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("LTC/USDT", "1h", candle_def),
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("LTC/BTC", "1h", candle_def),
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("XRP/USDT", "30m", candle_def),
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("LTC/USDT", "30m", candle_def),
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("NEO/USDT", "1h", candle_def),
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("NEO/USDT", "30m", candle_def),
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("NEO/USDT", "5m", candle_def),
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("NEO/USDT", "15m", candle_def),
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("NEO/USDT", "2h", CandleType.FUTURES),
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("ETH/BTC", "1h", CandleType.SPOT), # One candle remains as spot
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("ETH/USDT", "30m", candle_def),
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]
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for inf_pair in informative_pairs:
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assert inf_pair in strategy.gather_informative_pairs()
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def test_historic_ohlcv(pair, timeframe, candle_type):
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return data[
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(pair, timeframe or strategy.timeframe, CandleType.from_string(candle_type))
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].copy()
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mocker.patch(
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"freqtrade.data.dataprovider.DataProvider.historic_ohlcv", side_effect=test_historic_ohlcv
|
|
)
|
|
|
|
analyzed = strategy.advise_all_indicators(
|
|
{p: data[(p, strategy.timeframe, candle_def)] for p in ("XRP/USDT", "LTC/USDT")}
|
|
)
|
|
expected_columns = [
|
|
"rsi_1h",
|
|
"rsi_30m", # Stacked informative decorators
|
|
"neo_usdt_rsi_1h", # NEO 1h informative
|
|
"rsi_NEO_USDT_neo_usdt_NEO/USDT_30m", # Column formatting
|
|
"rsi_from_callable", # Custom column formatter
|
|
"eth_btc_rsi_1h", # Quote currency not matching stake currency
|
|
"rsi",
|
|
"rsi_less", # Non-informative columns
|
|
"rsi_5m", # Manual informative dataframe
|
|
]
|
|
for _, dataframe in analyzed.items():
|
|
for col in expected_columns:
|
|
assert col in dataframe.columns
|