diff --git a/smartmoneyconcepts/smc.py b/smartmoneyconcepts/smc.py index eed5ad7..652e10e 100644 --- a/smartmoneyconcepts/smc.py +++ b/smartmoneyconcepts/smc.py @@ -710,32 +710,32 @@ def previous_high_low(cls, ohlc: DataFrame, time_frame: str = "1D") -> Series: broken_high = np.zeros(len(ohlc), dtype=np.int32) broken_low = np.zeros(len(ohlc), dtype=np.int32) + resampled_ohlc = ohlc.resample(time_frame).agg( + { + "open": "first", + "high": "max", + "low": "min", + "close": "last", + "volume": "sum", + } + ).dropna() + currently_broken_high = False currently_broken_low = False last_broken_time = None for i in range(len(ohlc)): - resampled_ohlc = ( - ohlc[:i] - .resample(time_frame) - .agg( - { - "open": "first", - "high": "max", - "low": "min", - "close": "last", - "volume": "sum", - } - ) - ) - if len(resampled_ohlc) >= 1: - if last_broken_time != resampled_ohlc.index[-1]: - currently_broken_high = False - currently_broken_low = False - last_broken_time = resampled_ohlc.index[-1] # remove rows with nan values (ignoring weekends) - resampled_ohlc = resampled_ohlc.dropna() - previous_high[i] = resampled_ohlc["high"].iloc[-2] if len(resampled_ohlc) > 1 else np.nan - previous_low[i] = resampled_ohlc["low"].iloc[-2] if len(resampled_ohlc) > 1 else np.nan + resampled_previous_index = np.where( + resampled_ohlc.index < ohlc.index[i] + )[0] + if len(resampled_previous_index) <= 1: + previous_high[i] = np.nan + previous_low[i] = np.nan + continue + resampled_previous_index = resampled_previous_index[-1] + + previous_high[i] = resampled_ohlc["high"].iloc[resampled_previous_index] + previous_low[i] = resampled_ohlc["low"].iloc[resampled_previous_index] currently_broken_high = ohlc["high"].iloc[i] > previous_high[i] or currently_broken_high currently_broken_low = ohlc["low"].iloc[i] < previous_low[i] or currently_broken_low broken_high[i] = 1 if currently_broken_high else 0 diff --git a/tests/conftest.py b/tests/conftest.py index 34cb27a..5b1fa1c 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -59,7 +59,7 @@ def previous_high_low_result_data(): test_instrument = "EURUSD" return pd.read_csv( os.path.join( - "tests/test_data", test_instrument, "previous_high_low_result_data.csv" + "tests/test_data", test_instrument, "previous_high_low_result_data_4h.csv" ) ) diff --git a/tests/test_data/EURUSD/previous_high_low_result_data.csv b/tests/test_data/EURUSD/previous_high_low_result_data_1D.csv similarity index 100% rename from tests/test_data/EURUSD/previous_high_low_result_data.csv rename to tests/test_data/EURUSD/previous_high_low_result_data_1D.csv diff --git a/tests/test_data/EURUSD/previous_high_low_result_data_4h.csv b/tests/test_data/EURUSD/previous_high_low_result_data_4h.csv new file mode 100644 index 0000000..d0f74e5 --- /dev/null +++ b/tests/test_data/EURUSD/previous_high_low_result_data_4h.csv @@ -0,0 +1,24425 @@ +PreviousHigh,PreviousLow,BrokenHigh,BrokenLow +,,0,0 +,,0,0 +,,0,0 +,,0,0 +,,0,0 +,,0,0 +,,0,0 +,,0,0 +,,0,0 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"test_data", test_instrument, "previous_high_low_result_data_4h.csv" + ) + ) + print("previous_high_low test time: ", time.time() - start_time) + pd.testing.assert_frame_equal(previous_high_low_data, previous_high_low_result_data, check_dtype=False) + + # test 1D time frame + start_time = time.time() + previous_high_low_data = smc.previous_high_low(df, time_frame="1D") + previous_high_low_result_data = pd.read_csv( + os.path.join( + "test_data", test_instrument, "previous_high_low_result_data_1D.csv" + ) + ) + print("previous_high_low test time: ", time.time() - start_time) + pd.testing.assert_frame_equal(previous_high_low_data, previous_high_low_result_data, check_dtype=False) + + # test W time frame + start_time = time.time() + previous_high_low_data = smc.previous_high_low(df, time_frame="W") + previous_high_low_result_data = pd.read_csv( + os.path.join( + "test_data", test_instrument, "previous_high_low_result_data_W.csv" ) ) print("previous_high_low test time: ", time.time() - start_time) @@ -106,51 +129,63 @@ def test_retracements(self): # def generate_results_data(): -# fvg_data = smc.fvg(df, join_consecutive=True) -# fvg_data.to_csv( -# os.path.join("test_data", test_instrument, "fvg_result_data.csv"), index=False -# ) - -# swing_highs_lows_data = smc.swing_highs_lows(df, swing_length=5) -# swing_highs_lows_data.to_csv( -# os.path.join("test_data", test_instrument, "swing_highs_lows_result_data.csv"), -# index=False, -# ) - -# bos_choch_data = smc.bos_choch(df, swing_highs_lows_data) -# bos_choch_data.to_csv( -# os.path.join("test_data", test_instrument, "bos_choch_result_data.csv"), -# index=False, -# ) - -# ob_data = smc.ob(df, swing_highs_lows_data) -# ob_data.to_csv( -# os.path.join("test_data", test_instrument, "ob_result_data.csv"), index=False -# ) - -# liquidity_data = smc.liquidity(df, swing_highs_lows_data) -# liquidity_data.to_csv( -# os.path.join("test_data", test_instrument, "liquidity_result_data.csv"), -# index=False, -# ) - -# previous_high_low_data = smc.previous_high_low(df, time_frame="4h") -# previous_high_low_data.to_csv( -# os.path.join("test_data", test_instrument, "previous_high_low_result_data.csv"), -# index=False, -# ) - -# sessions = smc.sessions(df, session="London") -# sessions.to_csv( -# os.path.join("test_data", test_instrument, "sessions_result_data.csv"), -# index=False, -# ) - -# retracements = smc.retracements(df, swing_highs_lows_data) -# retracements.to_csv( -# os.path.join("test_data", test_instrument, "retracements_result_data.csv"), -# index=False, -# ) + # fvg_data = smc.fvg(df, join_consecutive=True) + # fvg_data.to_csv( + # os.path.join("test_data", test_instrument, "fvg_result_data.csv"), index=False + # ) + + # swing_highs_lows_data = smc.swing_highs_lows(df, swing_length=5) + # swing_highs_lows_data.to_csv( + # os.path.join("test_data", test_instrument, "swing_highs_lows_result_data.csv"), + # index=False, + # ) + + # bos_choch_data = smc.bos_choch(df, swing_highs_lows_data) + # bos_choch_data.to_csv( + # os.path.join("test_data", test_instrument, "bos_choch_result_data.csv"), + # index=False, + # ) + + # ob_data = smc.ob(df, swing_highs_lows_data) + # ob_data.to_csv( + # os.path.join("test_data", test_instrument, "ob_result_data.csv"), index=False + # ) + + # liquidity_data = smc.liquidity(df, swing_highs_lows_data) + # liquidity_data.to_csv( + # os.path.join("test_data", test_instrument, "liquidity_result_data.csv"), + # index=False, + # ) + + # previous_high_low_data = smc.previous_high_low(df, time_frame="4h") + # previous_high_low_data.to_csv( + # os.path.join("test_data", test_instrument, "previous_high_low_result_data_4h.csv"), + # index=False, + # ) + + # previous_high_low_data = smc.previous_high_low(df, time_frame="1D") + # previous_high_low_data.to_csv( + # os.path.join("test_data", test_instrument, "previous_high_low_result_data_1D.csv"), + # index=False, + # ) + + # previous_high_low_data = smc.previous_high_low(df, time_frame="W") + # previous_high_low_data.to_csv( + # os.path.join("test_data", test_instrument, "previous_high_low_result_data_W.csv"), + # index=False, + # ) + + # sessions = smc.sessions(df, session="London") + # sessions.to_csv( + # os.path.join("test_data", test_instrument, "sessions_result_data.csv"), + # index=False, + # ) + + # retracements = smc.retracements(df, swing_highs_lows_data) + # retracements.to_csv( + # os.path.join("test_data", test_instrument, "retracements_result_data.csv"), + # index=False, + # ) # generate_results_data()