improved highs_lows function by replacing the thresh with a percentag…

…e, and fixxed liquidity function

build/lib/smartmoneyconcepts/smc.py

@@ -84,6 +84,7 @@ def fvg(cls, ohlc: DataFrame) -> Series:
 
         mitigated_index = np.zeros(len(ohlc), dtype=np.int32)
         for i in np.where(fvg != 0)[0]:
+            mask = np.zeros(len(ohlc), dtype=np.bool)
             if fvg[i] == 1:
                 mask = ohlc["low"][i + 2 :] <= top[i]
             elif fvg[i] == -1:
@@ -150,12 +151,19 @@ def ob(cls, ohlc: DataFrame) -> Series:
         fvg = cls.fvg(ohlc)
 
         ob = np.where((fvg["FVG"].shift(-1) != 0) & (fvg["FVG"] == 0), fvg["FVG"].shift(-1), 0)
-        top = np.where((fvg["FVG"].shift(-1) == -1) & (ohlc["high"] < ohlc["high"].shift(-1)), ohlc["high"].shift(-1), ohlc["high"])
-        bottom = np.where((fvg["FVG"].shift(-1) == 1) & (ohlc["low"] > ohlc["low"].shift(-1)), ohlc["low"].shift(-1), ohlc["low"])
+        # top is equal to the current candles high unless the ob is -1 and the next candles high is higher than the current candles high then top is equal to the next candles high
+        top = np.where(
+            (ob == -1) & (ohlc["high"].shift(-1) > ohlc["high"]), ohlc["high"].shift(-1), ohlc["high"]
+        )
+        # bottom is equal to the current candles low unless the ob is 1 and the next candles low is lower than the current candles low then bottom is equal to the next candles low
+        bottom = np.where(
+            (ob == 1) & (ohlc["low"].shift(-1) < ohlc["low"]), ohlc["low"].shift(-1), ohlc["low"]
+        )
 
         # set mitigated to np.nan
         mitigated_index = np.zeros(len(ohlc), dtype=np.int32)
         for i in np.where(ob != 0)[0]:
+            mask = np.zeros(len(ohlc), dtype=np.bool)
             if ob[i] == 1:
                 mask = ohlc["low"][i + 2 :] <= top[i]
             elif ob[i] == -1:
@@ -261,3 +269,6 @@ def liquidity(cls, ohlc: DataFrame, range_percent=0.01, up_thresh=0.05, down_thr
         return pd.concat(
             [liquidity, buy_sell_side, level, liquidity_end, liquidity_swept], axis=1
         )
+
+
+# TODO: correct mask error for ob and fvg

smartmoneyconcepts.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: smartmoneyconcepts
-Version: 0.0.8
+Version: 0.0.9
 Summary: Getting indicators based on smart money concepts or ICT
 Home-page: https://github.com/joshyattridge/smartmoneyconcepts
 Author: Joshua Attridge

smartmoneyconcepts/SMC.py

@@ -104,8 +104,11 @@ def fvg(cls, ohlc: DataFrame) -> Series:
         )
 
     @classmethod
-    def highs_lows(cls, ohlc: DataFrame, up_thresh=0.05, down_thresh=-0.05) -> Series:
-        highs_lows = peak_valley_pivots(ohlc["close"], up_thresh, down_thresh)
+    def highs_lows(cls, ohlc: DataFrame, percentage_thresh=0.05) -> Series:
+        # subtract the highest high from the lowest low
+        pip_range = max(ohlc["high"]) - min(ohlc["low"])
+        pip_range = pip_range * percentage_thresh
+        highs_lows = peak_valley_pivots(ohlc["close"], pip_range, -pip_range)
         still_adjusting = True
         while still_adjusting:
             still_adjusting = False
@@ -183,7 +186,7 @@ def ob(cls, ohlc: DataFrame) -> Series:
         )
 
     @classmethod
-    def liquidity(cls, ohlc: DataFrame, range_percent=0.01, up_thresh=0.05, down_thresh=-0.05) -> Series:
+    def liquidity(cls, ohlc: DataFrame, range_percent=0.01) -> Series:
         """
         Liquidity
         Liquidity is when there are multiply highs within a small range of each other.
@@ -194,14 +197,13 @@ def liquidity(cls, ohlc: DataFrame, range_percent=0.01, up_thresh=0.05, down_thr
         pip_range = (max(ohlc["high"]) - min(ohlc["low"])) * range_percent
 
         # get the highs and lows
-        highs_lows = cls.highs_lows(ohlc, up_thresh, down_thresh)
+        highs_lows = cls.highs_lows(ohlc)
         levels = highs_lows["Levels"]
         highs_lows = highs_lows["HighsLows"]
 
         # go through all of the high levels and if there are more than 1 within the pip range, then it is liquidity
         liquidity = np.zeros(len(ohlc), dtype=np.int32)
-        buy_sell_side = np.zeros(len(ohlc), dtype=np.int32)
-        liquidity_level = np.zeros(len(ohlc), dtype=np.int32)
+        liquidity_level = np.zeros(len(ohlc), dtype=np.float32)
         liquidity_end = np.zeros(len(ohlc), dtype=np.int32)
         liquidity_swept = np.zeros(len(ohlc), dtype=np.int32)
 
@@ -227,7 +229,6 @@ def liquidity(cls, ohlc: DataFrame, range_percent=0.01, up_thresh=0.05, down_thr
                         temp_liquidity_levels
                     )
                     liquidity[i] = 1
-                    buy_sell_side[i] = 2  # 2 is buy
                     liquidity_level[i] = average_high
                     liquidity_end[i] = end
                     liquidity_swept[i] = swept
@@ -254,21 +255,16 @@ def liquidity(cls, ohlc: DataFrame, range_percent=0.01, up_thresh=0.05, down_thr
                     average_low = sum(temp_liquidity_levels) / len(
                         temp_liquidity_levels
                     )
-                    liquidity[i] = 1
-                    buy_sell_side[i] = 1
+                    liquidity[i] = -1
                     liquidity_level[i] = average_low
                     liquidity_end[i] = end
                     liquidity_swept[i] = swept
 
         liquidity = pd.Series(liquidity, name="Liquidity")
-        buy_sell_side = pd.Series(buy_sell_side, name="BuySellSide")
         level = pd.Series(liquidity_level, name="Level")
         liquidity_end = pd.Series(liquidity_end, name="End")
         liquidity_swept = pd.Series(liquidity_swept, name="Swept")
 
         return pd.concat(
-            [liquidity, buy_sell_side, level, liquidity_end, liquidity_swept], axis=1
-        )
-
-
-# TODO: correct mask error for ob and fvg
+            [liquidity, level, liquidity_end, liquidity_swept], axis=1
+        )

tests/SMC.test.py

@@ -8,7 +8,7 @@
 from smartmoneyconcepts.smc import smc
 
 df = pd.read_csv("EURUSD_15M.csv")
-df = df.iloc[-200:]
+df = df.iloc[-5000:]
 df = df.reset_index(drop=True)
 fig = go.Figure(
     data=[
@@ -48,7 +48,7 @@ def add_FVG(fig):
     return fig
 
 def add_highs_lows(fig):
-    highs_lows_data = smc.highs_lows(df, up_thresh=0.0005, down_thresh=-0.0005)
+    highs_lows_data = smc.highs_lows(df)
 
     # remove from highs_lows_data
     indexs = []
@@ -104,7 +104,7 @@ def add_liquidity(fig):
 
     # draw a line horizontally for each liquidity level
     for i in range(len(liquidity_data["Liquidity"])):
-        if liquidity_data["Liquidity"][i] == 1:
+        if liquidity_data["Liquidity"][i] != 0:
             fig.add_trace(
                 go.Scatter(
                     x=[df["date"][i], df["date"][liquidity_data["End"][i]]],
@@ -127,7 +127,7 @@ def add_liquidity(fig):
                         liquidity_data["Level"][i],
                         (
                             df["high"][liquidity_data["Swept"][i]]
-                            if liquidity_data["BuySellSide"][i] == 2
+                            if liquidity_data["Liquidity"][i] == 1
                             else df["low"][liquidity_data["Swept"][i]]
                         ),
                     ],
@@ -140,8 +140,8 @@ def add_liquidity(fig):
     return fig
 
 
-fig = add_FVG(fig)
+# fig = add_FVG(fig)
 fig = add_highs_lows(fig)
-fig = add_OB(fig)
+# fig = add_OB(fig)
 fig = add_liquidity(fig)
 fig.show()