Merge pull request #15 from joshyattridge/previous_candles_high_low

Previous candles high low

README.md

@@ -2,7 +2,7 @@
 
 The Smart Money Concepts Python Indicator is a sophisticated financial tool developed for traders and investors to gain insights into market sentiment, trends, and potential reversals. This indicator is inspired by Inner Circle Trader (ICT) concepts like Order blocks, Liquidity, Fair Value Gap, Swing Highs and Lows, Break of Structure, Change of Character, and more. Please Take a look and contribute to the project.
 
-![alt text](https://github.com/joshyattridge/smart-money-concepts/blob/21656dd807c4077f345b6cbf29b1bc37672628e9/tests/test_binance.png)
+![alt text](https://github.com/joshyattridge/smart-money-concepts/blob/53dd0b0a5e598d04b0cd3d10af71a0c252ad850d/tests/test_binance.png)
 
 ## Installation
 
@@ -109,6 +109,21 @@ Level = the level of the liquidity<br>
 End = the index of the last liquidity level<br>
 Swept = the index of the candle that swept the liquidity<br>
 
+### Previous High And Low
+
+```python
+smc.previous_high_low(ohlc, time_frame = "1D")
+```
+
+This method returns the previous high and low of the given time frame.
+
+parameters:<br>
+time_frame: str - the time frame to get the previous high and low 15m, 1H, 4H, 1D, 1W, 1M<br>
+
+returns:<br>
+PreviousHigh = the previous high<br>
+PreviousLow = the previous low<br>
+
 ## Contributing
 
 This project is still in BETA so please feel free to contribute to the project. By creating your own indicators or improving the existing ones. If you are stuggling to find something to do then please check out the issues tab for requested changes.

setup.py

@@ -2,7 +2,7 @@
 import codecs
 import os
 
-VERSION = '0.0.15'
+VERSION = '0.0.16'
 DESCRIPTION = 'Getting indicators based on smart money concepts or ICT'
 
 # read the contents of the README file

smartmoneyconcepts/smc.py

@@ -2,6 +2,7 @@
 import pandas as pd
 import numpy as np
 from pandas import DataFrame, Series
+from datetime import datetime
 
 
 def inputvalidator(input_="ohlc"):
@@ -50,7 +51,7 @@ def decorate(cls):
 
 @apply(inputvalidator(input_="ohlc"))
 class smc:
-    __version__ = "0.0.15"
+    __version__ = "0.0.16"
 
     @classmethod
     def fvg(cls, ohlc: DataFrame) -> Series:
@@ -642,3 +643,53 @@ def liquidity(cls, ohlc: DataFrame, swing_highs_lows: DataFrame, range_percent:f
         liquidity_swept = pd.Series(liquidity_swept, name="Swept")
 
         return pd.concat([liquidity, level, liquidity_end, liquidity_swept], axis=1)
+
+    @classmethod
+    def previous_high_low(cls, ohlc: DataFrame, time_frame: str = "1D") -> Series:
+        """
+        Previous High Low
+        This method returns the previous high and low of the given time frame.
+
+        parameters:
+        time_frame: str - the time frame to get the previous high and low 15m, 1H, 4H, 1D, 1W, 1M
+
+        returns:
+        PreviousHigh = the previous high
+        PreviousLow = the previous low
+        """
+
+        ohlc.index = pd.to_datetime(ohlc.index)
+
+        resampled_ohlc = ohlc.resample(time_frame).agg(
+            {
+                "open": "first",
+                "high": "max",
+                "low": "min",
+                "close": "last",
+                "volume": "sum",
+            }
+        )
+
+        # for every candle in ohlc add a new column with the previous high and low
+        # Fix: Import the datetime module
+        previous_high = np.zeros(len(ohlc), dtype=np.float32)
+        previous_low = np.zeros(len(ohlc), dtype=np.float32)
+
+        for i in range(len(ohlc)):
+            current_time = ohlc.index[i]
+            # get the 1st high where the current time is greater than the time from the resampled ohlc
+            previous_high_index = resampled_ohlc["high"].where(
+                resampled_ohlc.index < current_time
+            ).last_valid_index()
+            previous_high[i] = resampled_ohlc["high"][previous_high_index] if previous_high_index is not None else np.nan
+            # get the 1st low where the current time is greater than the time from the resampled ohlc
+            previous_low_index = resampled_ohlc["low"].where(
+                resampled_ohlc.index < current_time
+            ).last_valid_index()
+            previous_low[i] = resampled_ohlc["low"][previous_low_index] if previous_low_index is not None else np.nan
+
+        previous_high = pd.Series(previous_high, name="PreviousHigh")
+        previous_low = pd.Series(previous_low, name="PreviousLow")
+
+        return pd.concat([previous_high, previous_low], axis=1)
+

tests/SMC.test_binance.py

@@ -260,16 +260,50 @@ def add_liquidity(fig, liquidity_data):
             )
     return fig
 
+def add_previous_high_low(fig, previous_high_low_data):
+    high = previous_high_low_data["PreviousHigh"]
+    low = previous_high_low_data["PreviousLow"]
+    # draw a line horizontally for each high where the highs are the same consecutively
+    for i in range(len(high)-1):
+        if high.iloc[i] == high.iloc[i+1]:
+            fig.add_trace(
+                go.Scatter(
+                    x=[df.index[i], df.index[i+1]],
+                    y=[high.iloc[i], high.iloc[i+1]],
+                    mode="lines",
+                    line=dict(
+                        color="lightblue",
+                    ),
+                )
+            )
+    # draw a line horizontally for each low where the lows are the same consecutively
+    for i in range(len(low)-1):
+        if low.iloc[i] == low.iloc[i+1]:
+            fig.add_trace(
+                go.Scatter(
+                    x=[df.index[i], df.index[i+1]],
+                    y=[low.iloc[i], low.iloc[i+1]],
+                    mode="lines",
+                    line=dict(
+                        color="lightblue",
+                    ),
+                )
+            )
+
+    return fig
+
 fvg_data = smc.fvg(df)
 swing_highs_lows_data = smc.swing_highs_lows(df, swing_length=50)
 bos_choch_data = smc.bos_choch(df, swing_highs_lows_data)
 ob_data = smc.ob(df, swing_highs_lows_data)
 liquidity_data = smc.liquidity(df, swing_highs_lows_data)
+previous_high_low_data = smc.previous_high_low(df, time_frame="1W")
 fig = add_FVG(fig, fvg_data)
 fig = add_swing_highs_lows(fig, swing_highs_lows_data)
 fig = add_bos_choch(fig, bos_choch_data)
 fig = add_OB(fig, ob_data)
 fig = add_liquidity(fig, liquidity_data)
+fig = add_previous_high_low(fig, previous_high_low_data)
 
 fig.update_layout(xaxis_rangeslider_visible=False)
 fig.update_layout(showlegend=False)