diff --git a/indicators/Pivots.py b/indicators/Pivots.py
new file mode 100644
index 0000000..37db440
--- /dev/null
+++ b/indicators/Pivots.py
@@ -0,0 +1,123 @@
+import pandas as pd
+import numpy as np
+
+def Pivots(dataframe, start_hour=8, start_minute=20, days_to_plot=0):
+    df = dataframe.copy()
+    
+    # Calculate the number of minutes in a day
+    minutes_per_day = 24 * 60
+    
+    # Calculate the start time in minutes
+    start_time_minutes = start_hour * 60 + start_minute
+    
+    # Calculate the closing time in minutes
+    close_time_minutes = start_time_minutes - df["timeframe"].iloc[0]
+    
+    # Ensure close_time_minutes is positive
+    if close_time_minutes < 0:
+        close_time_minutes += minutes_per_day
+    
+    # Calculate the number of bars in a day
+    bars_per_day = minutes_per_day / df["timeframe"].iloc[0]
+    
+    # Initialize pivot level dictionaries
+    pivot_levels = {
+        "P": [],
+        "R1": [],
+        "R2": [],
+        "R3": [],
+        "S1": [],
+        "S2": [],
+        "S3": []
+    }
+
+    for i in range(len(df)):
+        # Calculate the previous day's opening and closing bars
+        previous_closing_bar = find_last_time_match_fast(close_time_minutes, i + 1, df, True)
+        
+        if df["time"].iloc[previous_closing_bar] != df["time"].iloc[previous_closing_bar - 1]:
+            previous_opening_bar = find_last_time_match_fast(start_time_minutes, previous_closing_bar + 1, df, False)
+            previous_high = df["high"].iloc[previous_closing_bar]
+            previous_low = df["low"].iloc[previous_closing_bar]
+            previous_close = df["close"].iloc[previous_closing_bar]
+            
+            # Calculate the previous day's high and low
+            for j in range(previous_closing_bar, previous_opening_bar + 1):
+                if df["high"].iloc[j] > previous_high:
+                    previous_high = df["high"].iloc[j]
+                if df["low"].iloc[j] < previous_low:
+                    previous_low = df["low"].iloc[j]
+            
+            # Calculate pivot levels
+            P = (previous_high + previous_low + previous_close) / 3
+            R1 = (2 * P) - previous_low
+            S1 = (2 * P) - previous_high
+            R2 = P + (previous_high - previous_low)
+            S2 = P - (previous_high - previous_low)
+            R3 = P + 2 * (previous_high - previous_low)
+            S3 = P - 2 * (previous_high - previous_low)
+            
+            pivot_levels["P"].append(P)
+            pivot_levels["R1"].append(R1)
+            pivot_levels["R2"].append(R2)
+            pivot_levels["R3"].append(R3)
+            pivot_levels["S1"].append(S1)
+            pivot_levels["S2"].append(S2)
+            pivot_levels["S3"].append(S3)
+        else:
+            # If the previous closing bar is the same as the current closing bar,
+            # just append NaN values to the pivot level buffers
+            pivot_levels["P"].append(np.nan)
+            pivot_levels["R1"].append(np.nan)
+            pivot_levels["R2"].append(np.nan)
+            pivot_levels["R3"].append(np.nan)
+            pivot_levels["S1"].append(np.nan)
+            pivot_levels["S2"].append(np.nan)
+            pivot_levels["S3"].append(np.nan)
+    
+    # Filter the DataFrame based on days_to_plot
+    if days_to_plot > 0:
+        df = df.iloc[-int(days_to_plot * bars_per_day):]
+
+    return pivot_levels
+
+def find_last_time_match_fast(time_to_look_for, starting_bar, df, is_closing_bar):
+    how_many_bars_back = min(len(df) - 1, int(3 * 1440 / df["timeframe"].iloc[0]))
+
+    if check_bar_is_what_we_look_for(time_to_look_for, starting_bar, df, is_closing_bar):
+        return starting_bar
+    elif starting_bar < how_many_bars_back and check_bar_is_what_we_look_for(time_to_look_for, starting_bar, df, is_closing_bar):
+        return starting_bar
+    elif starting_bar < how_many_bars_back and check_bar_is_what_we_look_for(time_to_look_for, starting_bar + 1, df, is_closing_bar):
+        return starting_bar + 1
+    else:
+        for a in range(starting_bar + 1, how_many_bars_back):
+            if check_bar_is_what_we_look_for(time_to_look_for, a, df, is_closing_bar):
+                return a
+    return how_many_bars_back + 1
+
+def check_bar_is_what_we_look_for(time_to_look_for, bar, df, is_closing_bar):
+    if bar >= len(df) - 1:
+        return False
+
+    previous_bars_time = (df["time"].iloc[bar - 1].hour * 60) + df["time"].iloc[bar - 1].minute
+    current_bars_time = (df["time"].iloc[bar].hour * 60) + df["time"].iloc[bar].minute
+
+    if current_bars_time == time_to_look_for:
+        return True
+
+    previous_bar_day = df["time"].iloc[bar - 1].dayofyear
+    current_bar_day = df["time"].iloc[bar].dayofyear
+
+    if current_bar_day != previous_bar_day:
+        current_bars_time -= 1440
+
+    if previous_bars_time > time_to_look_for and current_bars_time < time_to_look_for:
+        return is_closing_bar if current_bars_time < time_to_look_for else True
+
+    return False
+
+# Example usage:
+# Replace df with your pandas DataFrame containing columns ['time', 'high', 'low', 'open', 'close', 'volume', 'timeframe']
+# Call the function with your desired parameters
+#pivots = calculate_pivots(df, start_hour=8, start_minute=20, days_to_plot=0)