giobel · November 25, 2025 06:04
diff --git a/*Misc Python* b/*Misc Python*
 Collection of misc python scripts
diff --git a/appearanceTemplates.py b/appearanceTemplates.py
 from revizto_decoder import decode_aptmpl
 import zlib, re, binascii, json
 import blackboxprotobuf
 import base64
 import inspect
 import random
 import colorsys


 def color_to_int(r, g, b, a=255, order='RGBA'):
    """
    Convert RGBA components (0–255) to a 32-bit integer.
    order = 'RGBA' or 'ARGB' (Revizto-like).
    """
    if order.upper() == 'RGBA':
        return (r << 24) | (g << 16) | (b << 8) | a
    elif order.upper() == 'ARGB':
        return (a << 24) | (r << 16) | (g << 8) | b
    else:
        raise ValueError("order must be 'RGBA' or 'ARGB'")

 def random_color_int():
    """Generate a random RGBA color encoded as an integer."""
    r = random.randint(0, 255)
    g = random.randint(0, 255)
    b = random.randint(0, 255)
    a = 255  # fully opaque
    return color_to_int(r, g, b, a)

 def distinct_colors_int(n):
    """Generate n visually distinct colors encoded as 32-bit integers."""
    colors = []
    for i in range(n):
        hue = i / n  # evenly spaced hues around the color wheel
        r, g, b = colorsys.hsv_to_rgb(hue, 0.85, 1.0)  # bright and saturated
        r, g, b = int(r * 255), int(g * 255), int(b * 255)
        colors.append(color_to_int(r, g, b))
    return colors


 def varName(var):
    lcls = inspect.stack()[2][0].f_locals
    for name in lcls:
        if id(var) == id(lcls[name]):
            return name
    return None

 def printStars(message):
    try:
        print (f"********{varName(message).upper()}**********\n", message)
    except:
        print('**************\n',message)

 path = 'test.aptmpl'

 with open(path, "rb") as f:
    data = f.read()
    printStars(data)
        # b'\n\tAppearSet\x10\x01\x18\x00"o\x10\x00\x18\x00"\x04test(\x002a\x08\x00\x11.\x1e\\\xb8\xa4\xf3V\x1a\x1aTx\x9c\xe3\xf2\xe7`\x16`\x90\xf2\xe4`\x10r\xe5r\x16r\x04\xd2\[email protected]\n$\xf9\x85x\x83\x83\x9c\xe3\xdd\x9c\x1c\xe3\x83R\x93\x12\x8b\xa4\x84\x85\x04]R\x93\xb3\x15\x82S\xd3sS\xf3J\x14B*\x0bR\x9589X\x85X\x83\x1d\x83\x0c\x1d5\xd8$\x18\x00/\xfd\x0f\xd0**\n(\n$03830493-408b-4122-ba60-328f872ca1c9\x10\x00'
                    
    #ALL DECODED DATA
    decdata, typeof = blackboxprotobuf.decode_message(data)
    printStars(decdata)
        #  {'1': {'8': 8387201631772831856}, '2': 1, '3': 0, '4': {'2': 0, '3': 0, '4': b'test', '5': 0, '6': {'1': 0, '2': 1897972181776473646, '3': b'x\x9c\xe3\xf2\xe7`\x16`\x90\xf2\xe4`\x10r\xe5r\x16r\x04\xd2\[email protected]\n$\xf9\x85x\x83\x83\x9c\xe3\xdd\x9c\x1c\xe3\x83R\x93\x12\x8b\xa4\x84\x85\x04]R\x93\xb3\x15\x82S\xd3sS\xf3J\x14B*\x0bR\x9589X\x85X\x83\x1d\x83\x0c\x1d5\xd8$\x18\x00/\xfd\x0f\xd0'}}, '5': {'1': {'1': b'03830493-408b-4122-ba60-328f872ca1c9', '2': 0}}}
    
    # RENAME APPEARANCE TEMPLATE
    decdata['4']['4'] = 'Colour Search'

    # EXTRACT TEMPLATE PROPERTIES
    blob1 = decdata['4']['6']['3']
    printStars(blob1)
        #  b'x\x9c\xe3\xf2\xe7`\x16`\x90\xf2\xe4`\x10r\xe5r\x16r\x04\xd2\[email protected]\n$\xf9\x85x\x83\x83\x9c\xe3\xdd\x9c\x1c\xe3\x83R\x93\x12\x8b\xa4\x84\x85\x04]R\x93\xb3\x15\x82S\xd3sS\xf3J\x14B*\x0bR\x9589X\x85X\x83\x1d\x83\x0c\x1d5\xd8$\x18\x00/\xfd\x0f\xd0'

    #DECOMPRESS TEMPLATE PROPERTIES
    mes1 = zlib.decompress(blob1)
    printStars(mes1)
        # b'\nO\x08\x03\x10\x00\x1aI\x08\x00\x12E\nC\x12A\x08\x00\x12;\x08\x00\x127\x125\x08\x00\x12\x0f\x12\rSRC_FBA_Rebar\x1a\x13\x12\x11Deck Segment Type"\t\x08\x05\x12\x05SAR1A(\x06\x18\x00'

    # DECODE TEMPLATE PROPERTIES
    decItem, itemTypeOf = blackboxprotobuf.decode_message(mes1)
    printStars (decItem)

    print ('test \n', decItem['1'][0]['2'])
        # {'1': {'1': 3, '2': 0, '3': {'1': 0, '2': {'1': {'2': {'1': 0, '2': {'1': 0, '2': {'2': {'1': 0, '2': {'2': b'SRC_FBA_Rebar'}, '3': {'2': b'Deck Segment Type'}, '4': {'1': 5, '2': b'SAR1A'}, '5': 6}}}, '3': 0}}}}}}      

    #decItem['1'][0]['2'] = 1325369087 #4EFF86

    decItem['1'] = []
    
    template = {'1': 4, '2': 1325369087, '3': {'1': 0, '2': {'1': {'2': {'1': 0, '2': {'1': 0, '2': {'2': {'1': 0, '2': {'2': b'SRC_FBA_Rebar'}, '3': {'2': b'Slice Number'}, '4': {'1': 5, '2': b'S001'}, '5': 6}}}, '3': 1}}}}}

    print ('template', template['2'])

    for i in range(0,220):
        entry = template.copy()
        import copy
        entry = copy.deepcopy(template)  # deep copy to avoid shared refs
        #colour
        entry['2'] = random_color_int()
        #property name
        entry['3']['2']['1']['2']['2']['2']['2']['4']['2'] = f"S{i:03}".encode('utf-8')  # makes b'S001' … b'S010'
        #print(template['3']['2']['1']['2']['2']['2']['2']['4']['2'])
        print (entry)
        decItem['1'].append(entry)

    #print (decItem)

    # ### property name
    # print ('property tab ', decItem['1']['3']['2']['1']['2']['2']['2']['2']['2']['2'])
    # print ('property name ', decItem['1']['3']['2']['1']['2']['2']['2']['2']['3']['2'])
    # print ('condition ', decItem['1']['3']['2']['1']['2']['2']['2']['2']['5']) #6=equal 7=not equal
    
    # # property tab 
    # decItem['1']['3']['2']['1']['2']['2']['2']['2']['2']['2'] = b'SRC_FBA'

    # # property name 
    # decItem['1']['3']['2']['1']['2']['2']['2']['2']['3']['2'] = b'Diameter'

    # #condition
    # decItem['1']['3']['2']['1']['2']['2']['2']['2']['5'] = 7

    # # RENAME PROPERTY VALUE
    # property_value = decItem['1']['3']['2']['1']['2']['2']['2']['2']['4']['2'] 
    # print ('property value ', property_value)
    # decItem['1']['3']['2']['1']['2']['2']['2']['2']['4']['2'] = b'170'
    
    #printStars (decItem)

    encItem = blackboxprotobuf.encode_message(decItem, itemTypeOf)

    #replaced message
    mes2 = zlib.compress(encItem)

    decdata['4']['6']['3'] = mes2

    data2 = blackboxprotobuf.encode_message(decdata, typeof)
    #printStars(data2)
    
    with open("Reencoded_AppearSet.aptmpl", "wb") as f:
        f.write(data2)

    print ('done')
diff --git a/autoClickScreenshtos.py b/autoClickScreenshtos.py
 import pyautogui
 import time
 import tkinter as tk
 from tkinter import messagebox
 from pynput import mouse
 from pynput import keyboard
 import math


 clicked_points = [] #start, step, end
 delay = 3

 def on_click(x, y, button, pressed):
    if pressed:
       #print(f"You clicked at ({x}, {y}) with {button}")
        clicked_points.append((x,y))
        # Stop after collecting 5 points (change as needed)
        if len(clicked_points) >= 3:
            #print("Captured 3 points:")
            for point in clicked_points:
                print(point)
            return False  # Stop listening

 def on_press(key):
    try:
        global delay
        #print(f"Key pressed: {key.char}")
        delay = int(key.char)
        return False
    except AttributeError:
        print(f"Special key pressed: {key}")

 root = tk.Tk()
 root.withdraw()  # Hide the main window
 root.attributes("-topmost", True)  # Make sure it's on top

 tk.messagebox.showinfo("ScreenGrabber", f"Please click start,offset and end point")

 with mouse.Listener(on_click=on_click) as listener:
    listener.join()


 screenWidth, screenHeight = pyautogui.size() # Get the size of the primary monitor.
 print (screenWidth, screenHeight)


 #time.sleep(3)  # Wait for the window to appear    

 currentMouseX, currentMouseY = pyautogui.position()

 print (currentMouseX, currentMouseY)

 start = clicked_points[0]
 end = clicked_points[2]
 step = clicked_points[1][1]-clicked_points[0][1]
 count = math.ceil((end[1] - start[1])/step) + 1

 print (f"length {end[1] - start[1]}")
 print (f"step {step}")
 print (f"count {count}")

 tk.messagebox.showinfo("ScreenGrabber", f"Click OK and type the delay in seconds. Default is 3 seconds")


 with keyboard.Listener(on_press=on_press) as listener:
    listener.join()

 time.sleep(1)



 for i in range(0,count):
    pyautogui.moveTo(start[0], start[1] + i * step)
    pyautogui.click()
    time.sleep(delay)  # Wait for the window to appear    
    timestamp = time.time()
    local_time = time.localtime(timestamp)
    formatted_time = time.strftime("%Y-%m-%d %H:%M:%S", local_time)
    formatted_time = formatted_time.replace(':','_')
    screenshot = pyautogui.screenshot()
    screenshot.save(formatted_time + "_FBA.png")

 tk.messagebox.showinfo("ScreenGrabber", f"Done")
diff --git a/outcomeCalculator.py b/outcomeCalculator.py
 import pandas as pd
 import yfinance as yf

 def adrp(df, length):
    ratio = df['High'] / df['Low']
    sma_ratio = ratio.rolling(length).mean()
    range_pct = round((sma_ratio - 1) * 100,2)
    return range_pct

 def check_price_streak(
    df,
    ticker,
    start_date,
    min_value,
    bought_price, 
    target_value,
 ):
    # Download historical data
    #df = yf.download(ticker,period="30d", interval="1d", progress=False, auto_adjust=True)
    
    a = adrp(df, 20)
    

    # Ensure datetime index
    df.index = pd.to_datetime(df.index)

    # Filter starting from the chosen date
    df = df.loc[start_date:]
    a = a.loc[start_date]
    print(f"ADR {round(a.item(),2)}%")

    risk = round(((bought_price/min_value) - 1) * 100,2)
    
    print (f"Risk {risk}%")
    
    if (risk > 0.66 * a.item()):
        print (f"Risk too high!!! Risk = {round(risk/a.item(),2)}")
    else:
        print (f"Risk < 0.66 x ADR. Acceptable Risk = {round(risk/a.item(),2)}")

    # Safely extract 'Close', 'High', and 'Low' prices as Series, handling MultiIndex if present
    close = None
    high = None
    low = None

    if isinstance(df.columns, pd.MultiIndex):
        close_col_tuple = ('Close', ticker)
        high_col_tuple = ('High', ticker)
        low_col_tuple = ('Low', ticker)

        # Attempt to get directly if columns exist as (ColumnType, Ticker)
        if close_col_tuple in df.columns:
            close = df[close_col_tuple]
        if high_col_tuple in df.columns:
            high = df[high_col_tuple]
        if low_col_tuple in df.columns:
            low = df[low_col_tuple]

        # Fallback if the MultiIndex structure isn't exactly ('ColumnType', Ticker)
        if close is None and 'Close' in df.columns and isinstance(df['Close'], pd.DataFrame) and ticker in df['Close'].columns:
            close = df['Close'][ticker]
        if high is None and 'High' in df.columns and isinstance(df['High'], pd.DataFrame) and ticker in df['High'].columns:
            high = df['High'][ticker]
        if low is None and 'Low' in df.columns and isinstance(df['Low'], pd.DataFrame) and ticker in df['Low'].columns:
            low = df['Low'][ticker]

        # Final fallback if still not found, and 'Close' / 'High' / 'Low' are top-level series
        if close is None and 'Close' in df.columns and isinstance(df['Close'], pd.Series):
            close = df['Close']
        if high is None and 'High' in df.columns and isinstance(df['High'], pd.Series):
            high = df['High']
        if low is None and 'Low' in df.columns and isinstance(df['Low'], pd.Series):
            low = df['Low']

    else:
        # Standard case: columns are flat, e.g., 'Close'
        close = df['Close']
        high = df['High']
        low = df['Low']

    # Check if we successfully extracted all series
    if close is None or high is None or low is None:
        raise ValueError(f"Could not reliably extract 'Close', 'High', or 'Low' price for {ticker} from DataFrame with unexpected column structure.")


    # Ensure 'close', 'high', and 'low' are Series. Squeeze if they are single-column DataFrames.
    if isinstance(close, pd.DataFrame) and len(close.columns) == 1:
        close = close.squeeze()
    elif not isinstance(close, pd.Series):
        raise ValueError("Expected 'close' to be a pandas Series but got a different type after extraction.")

    if isinstance(high, pd.DataFrame) and len(high.columns) == 1:
        high = high.squeeze()
    elif not isinstance(high, pd.Series):
        raise ValueError("Expected 'high' to be a pandas Series but got a different type after extraction.")

    if isinstance(low, pd.DataFrame) and len(low.columns) == 1:
        low = low.squeeze()
    elif not isinstance(low, pd.Series):
        raise ValueError("Expected 'low' to be a pandas Series but got a different type after extraction.")

    n = len(close) # Assuming 'high' and 'low' have the same length and index

    if n < 2:
        return {
            "days": 0,
            "last_date": None,
            "last_price": None,
            "roi": "N/A", 
            "reason": "Not enough data after start date"
        }

    streak = 2

    # Start from the *day after* the chosen start date
    for i in range(1, n):
        current_close_price = close.iloc[i].item()
        current_high_price = high.iloc[i].item()
        current_low_price = low.iloc[i].item()

    

        # Condition 1: Intraday low dropped below minimum value (most aggressive stop-loss)
        if current_low_price <= min_value:
            final_price_for_roi = min_value # Assume sold at min_value
            roi = f"Gain {round((final_price_for_roi - bought_price)/bought_price*100,2)}%"
            return {
                "days": streak,
                "last_date": close.index[i], # Date when the low occurred
                "last_price": current_low_price, # The actual low price that triggered
                "roi": roi,
                "reason": f"Intraday low dropped below minimum ({min_value})"
            }

        # Condition 2: Closing price dropped below minimum value
        if current_close_price <= min_value:
            final_price_for_roi = min_value # Assume sold at min_value
            roi = f"Gain {round((final_price_for_roi - bought_price)/bought_price*100,2)}%"
            return {
                "days": streak,
                "last_date": close.index[i], # Date when the close occurred
                "last_price": current_close_price, # The actual close price that triggered
                "roi": roi,
                "reason": f"Closing price dropped below minimum ({min_value})"
            }

        # Condition 3: Intraday high reached or exceeded target value
        if current_high_price >= target_value:
            final_price_for_roi = target_value # Assume sold at target price
            roi = f"Gain {round((final_price_for_roi - bought_price)/bought_price*100,2)}%"
            return {
                "days": streak,
                "last_date": close.index[i], # Date when the high occurred
                "last_price": current_high_price, # The actual high price that triggered
                "roi": roi,
                "reason": f"Intraday high reached or exceeded target ({target_value})"
            }

        # Condition 4: Closing price reached or exceeded target value (if not already met by high)
        if current_close_price >= target_value:
            final_price_for_roi = target_value # Assume sold at target price
            roi = f"Gain {round((final_price_for_roi - bought_price)/bought_price*100,2)}%"
            return {
                "days": streak,
                "last_date": close.index[i], # Date when the close occurred
                "last_price": current_close_price, # The actual close price that triggered
                "roi": roi,
                "reason": f"Closing price reached or exceeded target ({target_value})"
            }

        streak += 1

    # If we never broke any condition by the end of the data
    last_price_at_end = close.iloc[-1].item()
    roi_at_end = f"Gain {round((last_price_at_end - bought_price)/bought_price*100,2)}%"
    return {
        "days": streak,
        "last_date": close.index[-1],
        "last_price": last_price_at_end,
        "roi": roi_at_end,
        "reason": "Reached end of data"
    }


 ### INPUTS ###
 t = "NKTR"
 start_date = "2025-11-11"
 ### END ###
 data = yf.download(t, period="30d", interval="1d", progress=False, auto_adjust=True)
 #print (data)
 min_value = data.loc[start_date]['Low'].item()
 bought_price = data.loc[start_date]['High'].item()
 target_value = round(bought_price/min_value*bought_price,2)
 print(f"Bought Price ${round(bought_price,2)}")
 print(f"Bottom Price ${round(min_value,2)}")
 print(f"Target Price ${target_value}")
 print(f"Target Gain {round(((target_value/bought_price)-1)*100,2)}%")
 result = check_price_streak(
    df =data,
    ticker=t,
    start_date=start_date,
    min_value=min_value,
    bought_price=bought_price,
    target_value=target_value,
 )
 print(result)
diff --git a/triggerbars.py b/triggerbars.py
 import pandas as pd
 import yfinance as yf
 import pytz
 from datetime import datetime, time, timedelta

 # ===========================
 # Helper Functions
 # ===========================
 def adrp(df, length):
    """
    df: pandas DataFrame containing df['High'] and df['Low']
    length: SMA length (adrp_len)
    """
    ratio = df['High'] / df['Low']
    sma_ratio = ratio.rolling(length).mean()
    adrp_percent = 100 * (sma_ratio - 1)
    return adrp_percent

 def TestDate(df):
    df = df.copy()
    df.columns = [col[0] if isinstance(col, tuple) else col for col in df.columns]
    df["MA6"] = df["Close"].rolling(window=6, min_periods=6).mean()

    df_date = (df.loc["2025-11-07"])

    print (df_date)

    print('condition2', (df_date["High"] - df_date["Close"]) / df_date["High"]  )# Close near high (within 0.5%)
    print('condition3', (df_date["Low"] / df_date["Close"])  )# Low is within 7% of close
    print ('condition4a', (abs(df_date["Open"] - df_date["MA6"]) / df_date["MA6"]))
    print ('condition4b', (abs(df_date["Close"] - df_date["MA6"]) / df_date["MA6"]))

    condition1 = df_date["Open"] < df_date["Close"]  # Green candle
    condition2 = (df_date["High"] - df_date["Close"]) / df_date["High"] < 0.0055  # Close near high (within 0.5%)
    condition3 = (df_date["Low"] / df_date["Close"]) > 0.93  # Low is within 7% of close
    condition4 = (
        (abs(df_date["Open"] - df_date["MA6"]) / df_date["MA6"] < 0.018) |
        (abs(df_date["Close"] - df_date["MA6"]) / df_date["MA6"] < 0.018)
    )

    print (condition1, condition2, condition3, condition4)

 def GetTriggerBar(df):
    #TestDate(df)

    df = df.copy()
    df.columns = [col[0] if isinstance(col, tuple) else col for col in df.columns]
    # Compute 6-day moving average
    df["MA6"] = df["Close"].rolling(window=6, min_periods=6).mean()

    # Filter conditions:
    # 1. open < close (green candle)
    # 2. close is almost equal to high (close/high close to 1.0, difference < 1%)
    # 3. low/close < 3% (wick at bottom is small)

    condition1 = df["Open"] < df["Close"]  # Green candle
    condition2 = (df["High"] - df["Close"]) / df["High"] < 0.0055  # Close near high (within 0.5%)
    condition3 = (df["Low"] / df["Close"]) > 0.93  # Low is within 7% of close
    condition4 = (
        (abs(df["Open"] - df["MA6"]) / df["MA6"] < 0.018) |
        (abs(df["Close"] - df["MA6"]) / df["MA6"] < 0.018)
    )
    # Combine all conditions
    mask = condition1 & condition2 & condition3 & condition4


    # Print matching dates and values
    matching_dates = df[mask]
    cleaned_df = matching_dates.dropna(how='all')

    # Define the cutoff date (10 days ago)
    cutoff_date = datetime.now() - timedelta(days=10)

    # Filter only rows from the last 10 calendar days
    recent_matches = cleaned_df[cleaned_df.index >= cutoff_date]
    
    #add ADR check
    #add ma6 in uptrend

    #above 10 days MA6  cleaned_df["Close"].iloc[-1] > cleaned_df["MA6"].iloc[-1]:
    if not cleaned_df.empty and not recent_matches.empty:
        print (F'**********************************{t}****************************')
        # print("Dates matching criteria (Open < Close, Close ≈ High, Low/Close > 93%):")
        print(recent_matches[["Open", "Close", "High", "Low","Volume","MA6"]])

 # ===========================
 # Main Logic
 # ===========================
 # // Select all elements with the class
 # let elements = document.querySelectorAll('.tickerNameBox-GrtoTeat');

 """
 let elements = document.querySelectorAll('.tickerNameBox-GrtoTeat');
 let titles = Array.from(elements).map(el => el.title);
 copy(titles.join("\n"));  // copies directly to clipboard
 console.log("Copied to clipboard!");
 """
 clipboard_text = """
 RLMD − Relmada Therapeutics, Inc.
 IMMX − Immix Biopharma, Inc.
 IMTX − Immatics N.V.
 RPID − Rapid Micro Biosystems, Inc.
 PEPG − PepGen Inc.
 NMRA − Neumora Therapeutics, Inc.
 GALT − Galectin Therapeutics Inc.
 OLMA − Olema Pharmaceuticals, Inc.
 MOD − Modine Manufacturing Company
 TNGX − Tango Therapeutics, Inc.
 PLPC − Preformed Line Products Company
 LMND − Lemonade, Inc.
 OMER − Omeros Corporation
 OPXS − Optex Systems Holdings, Inc.
 LUMN − Lumen Technologies, Inc.
 UFPT − UFP Technologies, Inc.
 ARDX − Ardelyx, Inc.
 COCO − The Vita Coco Company, Inc.
 OPEN − Opendoor Technologies Inc
 LASR − nLIGHT, Inc.
 VHC − VirnetX Holding Corp
 TERN − Terns Pharmaceuticals, Inc.
 ESPR − Esperion Therapeutics, Inc.
 SMHI − SEACOR Marine Holdings Inc.
 ENTA − Enanta Pharmaceuticals, Inc.
 LAES − SEALSQ Corp
 REKR − Rekor Systems, Inc.
 PGEN − Precigen, Inc.
 OESX − Orion Energy Systems, Inc.
 AIP − Arteris, Inc.
 ANIX − Anixa Biosciences, Inc.
 LQDA − Liquidia Corporation
 TYGO − Tigo Energy, Inc.
 """

 #CHECK: KSS 07/11 NOT SHOWING, TTI 07/11 NOT SHOWING
 #MONITOR: TRVI, TTI, WDC, LXU, SWAG, OLMA


 tickers = [line.strip().split(' − ')[0] for line in clipboard_text.splitlines() if line.strip()]
 # tickers = ["TTI"]
 print ("Tickers to analyze:", tickers)

 ## TRIGGER BAR <3%

 for t in tickers:
    df = yf.download(t, period="30d", interval="1d", progress=False, auto_adjust=True)
    GetTriggerBar(df)

diff --git a/triggerWplot.py b/triggerWplot.py
 # ===========================
 # Main Logic
 # ===========================
 # // Select all elements with the class
 """
 let elements = document.querySelectorAll('.tickerNameBox-GrtoTeat');
 let titles = Array.from(elements).map(el => el.title);
 copy(titles.join("\n"));  // copies directly to clipboard
 console.log("Copied to clipboard!");
 """

 clipboard_text = """
 GRAL − GRAIL, Inc.
 SNDX − Syndax Pharmaceuticals, Inc.
 LMND − Lemonade, Inc.
 FSLY − Fastly, Inc.
 GANX − Gain Therapeutics, Inc.
 VTYX − Ventyx Biosciences, Inc.
 MTSI − MACOM Technology Solutions Holdings, Inc.
 SBH − Sally Beauty Holdings, Inc. (Name to be changed from Sally Holdings, Inc.)
 MKSI − MKS Inc.
 TRVI − Trevi Therapeutics, Inc.
 CELC − Celcuity Inc.
 TSEM − Tower Semiconductor Ltd.
 DYN − Dyne Therapeutics, Inc.
 KALV − KalVista Pharmaceuticals, Inc.
 MLYS − Mineralys Therapeutics, Inc.
 AMLX − Amylyx Pharmaceuticals, Inc.
 YOU − Clear Secure, Inc.
 FIGS − FIGS, Inc.
 """

 #tickers = [line.strip().split(' − ')[0] for line in clipboard_text.splitlines() if line.strip()]
 tickers = ["TTI"]
 print ("Tickers to analyze:", tickers)

 ## TRIGGER BAR <3%

 for t in tickers:
    # num_days = 10
    # start = plt.string_to_datetime('01/01/1900')
    # end = plt.today_datetime()
    # day_count = (end - start).days
    # print ('count', day_count)

    # df = yf.download(t, start, end, progress=False, auto_adjust=True)
    df = yf.download(t, period="10d", interval="1d", progress=False, auto_adjust=True)

    clean_df = GetTriggerBar(df)
    #print (df)
    opens = df['Open'][t].tolist()
    #print (opens)
    closes = df['Close'][t].tolist()
    #print (closes)
    highs = df['High'][t].tolist()
    #print (highs)
    lows = df['Low'][t].tolist()
    #print (lows)
    dates_str = plt.datetimes_to_string(df.index)
    #MA6
    y_ma6 = clean_df["MA6"].tolist()
    # print('clean def@!!!!!\n', clean_df_mas)
    #print (dates_str)
    data = {'Open':opens, 'Close':closes, 'High':highs, 'Low':lows}

    plt.clear_figure()  # clear previous plots
    # # # --- Candlestick plot ---
    clr = 236
    plt.canvas_color(clr)
    plt.axes_color(clr)
    plt.ticks_color('gray')
    # plt.ticks_style('italic')
    plt.plotsize(200, 30)


    plt.candlestick(dates_str, data)
    plt.title(f'{t}')


    plt.date_form(input_form='d/m/Y')
    #plt.date_form(output_form='d')
    #plt.xfrequency(day_count)

    tday = '24/11/2025'
    df.loc[start_date:]
    # plt.vline(tday, "red")
    min_lows = min(df['Low'][t].tolist())
    #plt.text(tday.split('/')[0], x = '24/11/2025', y = df.loc[tday]['Low'].item()-0.2)
    for i in range(len(dates_str)):
      #plt.text(dates_str[i].split('/')[0], x = dates_str[i], y = lows[i]-0.2)
      plt.text(dates_str[i][:2], x = dates_str[i], y = lows[i]-0.2)

    plt.xlabel("Date")
    plt.xticks([])  
    # xticks = range(len(dates_str))
    # xlabels = dates_str
    # plt.xticks(xticks, xlabels)
    plt.grid(True,True)
    plt.ylabel("Stock Price $")
    #print(dates_str)
    #print(y_ma6)
    default = min(y_ma6)
    cleaned = [default if (math.isnan(x)) else x for x in y_ma6]

    for i in range(len(dates_str)):
      #plt.text(dates_str[i].split('/')[0], x = dates_str[i], y = lows[i]-0.2)
      plt.text('-', x = dates_str[i], y = cleaned[i])

    # print(math.isnan(cleaned[0]))
    # #plt.scatter(y_ma6)
    # plt.plot(dates_str, cleaned, label = "plot",  style="dot")
    # #plt.plot(dates_str, y_ma6) 


    plt.show()
    plt.terminal_size()

    # box_data = []
    # for i in range(len(opens)):
    #     box_data.append([highs[i], closes[i], opens[i], lows[i]])
    # print (box_data)
    # # # Convert dates to strings for x-axis
    # print(dates_str)
    # plt.box(dates_str, box_data,width = 0.3)
    # plt.title("Dummy Google Stock Price Box Plot")
    # plt.xlabel("Date")
    # plt.ylabel("Stock Price $")
	from revizto_decoder import decode_aptmpl
	import zlib, re, binascii, json
	import blackboxprotobuf
	import base64
	import inspect
	import random
	import colorsys


	def color_to_int(r, g, b, a=255, order='RGBA'):
	"""
	Convert RGBA components (0–255) to a 32-bit integer.
	order = 'RGBA' or 'ARGB' (Revizto-like).
	"""
	if order.upper() == 'RGBA':
	return (r << 24) \| (g << 16) \| (b << 8) \| a
	elif order.upper() == 'ARGB':
	return (a << 24) \| (r << 16) \| (g << 8) \| b
	else:
	raise ValueError("order must be 'RGBA' or 'ARGB'")

	def random_color_int():
	"""Generate a random RGBA color encoded as an integer."""
	r = random.randint(0, 255)
	g = random.randint(0, 255)
	b = random.randint(0, 255)
	a = 255 # fully opaque
	return color_to_int(r, g, b, a)

	def distinct_colors_int(n):
	"""Generate n visually distinct colors encoded as 32-bit integers."""
	colors = []
	for i in range(n):
	hue = i / n # evenly spaced hues around the color wheel
	r, g, b = colorsys.hsv_to_rgb(hue, 0.85, 1.0) # bright and saturated
	r, g, b = int(r * 255), int(g * 255), int(b * 255)
	colors.append(color_to_int(r, g, b))
	return colors


	def varName(var):
	lcls = inspect.stack()[2][0].f_locals
	for name in lcls:
	if id(var) == id(lcls[name]):
	return name
	return None

	def printStars(message):
	try:
	print (f"******{varName(message).upper()}********\n", message)
	except:
	print('**************\n',message)

	path = 'test.aptmpl'

	with open(path, "rb") as f:
	data = f.read()
	printStars(data)
	# b'\n\tAppearSet\x10\x01\x18\x00"o\x10\x00\x18\x00"\x04test(\x002a\x08\x00\x11.\x1e\\\xb8\xa4\xf3V\x1a\x1aTx\x9c\xe3\xf2\xe7`\x16`\x90\xf2\xe4`\x10r\xe5r\x16r\x04\xd2\[email protected]\n$\xf9\x85x\x83\x83\x9c\xe3\xdd\x9c\x1c\xe3\x83R\x93\x12\x8b\xa4\x84\x85\x04]R\x93\xb3\x15\x82S\xd3sS\xf3J\x14B\x0bR\x9589X\x85X\x83\x1d\x83\x0c\x1d5\xd8$\x18\x00/\xfd\x0f\xd0*\n(\n$03830493-408b-4122-ba60-328f872ca1c9\x10\x00'

	#ALL DECODED DATA
	decdata, typeof = blackboxprotobuf.decode_message(data)
	printStars(decdata)
	# {'1': {'8': 8387201631772831856}, '2': 1, '3': 0, '4': {'2': 0, '3': 0, '4': b'test', '5': 0, '6': {'1': 0, '2': 1897972181776473646, '3': b'x\x9c\xe3\xf2\xe7`\x16`\x90\xf2\xe4`\x10r\xe5r\x16r\x04\xd2\[email protected]\n$\xf9\x85x\x83\x83\x9c\xe3\xdd\x9c\x1c\xe3\x83R\x93\x12\x8b\xa4\x84\x85\x04]R\x93\xb3\x15\x82S\xd3sS\xf3J\x14B*\x0bR\x9589X\x85X\x83\x1d\x83\x0c\x1d5\xd8$\x18\x00/\xfd\x0f\xd0'}}, '5': {'1': {'1': b'03830493-408b-4122-ba60-328f872ca1c9', '2': 0}}}

	# RENAME APPEARANCE TEMPLATE
	decdata['4']['4'] = 'Colour Search'

	# EXTRACT TEMPLATE PROPERTIES
	blob1 = decdata['4']['6']['3']
	printStars(blob1)
	# b'x\x9c\xe3\xf2\xe7`\x16`\x90\xf2\xe4`\x10r\xe5r\x16r\x04\xd2\[email protected]\n$\xf9\x85x\x83\x83\x9c\xe3\xdd\x9c\x1c\xe3\x83R\x93\x12\x8b\xa4\x84\x85\x04]R\x93\xb3\x15\x82S\xd3sS\xf3J\x14B*\x0bR\x9589X\x85X\x83\x1d\x83\x0c\x1d5\xd8$\x18\x00/\xfd\x0f\xd0'

	#DECOMPRESS TEMPLATE PROPERTIES
	mes1 = zlib.decompress(blob1)
	printStars(mes1)
	# b'\nO\x08\x03\x10\x00\x1aI\x08\x00\x12E\nC\x12A\x08\x00\x12;\x08\x00\x127\x125\x08\x00\x12\x0f\x12\rSRC_FBA_Rebar\x1a\x13\x12\x11Deck Segment Type"\t\x08\x05\x12\x05SAR1A(\x06\x18\x00'

	# DECODE TEMPLATE PROPERTIES
	decItem, itemTypeOf = blackboxprotobuf.decode_message(mes1)
	printStars (decItem)

	print ('test \n', decItem['1'][0]['2'])
	# {'1': {'1': 3, '2': 0, '3': {'1': 0, '2': {'1': {'2': {'1': 0, '2': {'1': 0, '2': {'2': {'1': 0, '2': {'2': b'SRC_FBA_Rebar'}, '3': {'2': b'Deck Segment Type'}, '4': {'1': 5, '2': b'SAR1A'}, '5': 6}}}, '3': 0}}}}}}

	#decItem['1'][0]['2'] = 1325369087 #4EFF86

	decItem['1'] = []

	template = {'1': 4, '2': 1325369087, '3': {'1': 0, '2': {'1': {'2': {'1': 0, '2': {'1': 0, '2': {'2': {'1': 0, '2': {'2': b'SRC_FBA_Rebar'}, '3': {'2': b'Slice Number'}, '4': {'1': 5, '2': b'S001'}, '5': 6}}}, '3': 1}}}}}

	print ('template', template['2'])

	for i in range(0,220):
	entry = template.copy()
	import copy
	entry = copy.deepcopy(template) # deep copy to avoid shared refs
	#colour
	entry['2'] = random_color_int()
	#property name
	entry['3']['2']['1']['2']['2']['2']['2']['4']['2'] = f"S{i:03}".encode('utf-8') # makes b'S001' … b'S010'
	#print(template['3']['2']['1']['2']['2']['2']['2']['4']['2'])
	print (entry)
	decItem['1'].append(entry)

	#print (decItem)

	# ### property name
	# print ('property tab ', decItem['1']['3']['2']['1']['2']['2']['2']['2']['2']['2'])
	# print ('property name ', decItem['1']['3']['2']['1']['2']['2']['2']['2']['3']['2'])
	# print ('condition ', decItem['1']['3']['2']['1']['2']['2']['2']['2']['5']) #6=equal 7=not equal

	# # property tab
	# decItem['1']['3']['2']['1']['2']['2']['2']['2']['2']['2'] = b'SRC_FBA'

	# # property name
	# decItem['1']['3']['2']['1']['2']['2']['2']['2']['3']['2'] = b'Diameter'

	# #condition
	# decItem['1']['3']['2']['1']['2']['2']['2']['2']['5'] = 7

	# # RENAME PROPERTY VALUE
	# property_value = decItem['1']['3']['2']['1']['2']['2']['2']['2']['4']['2']
	# print ('property value ', property_value)
	# decItem['1']['3']['2']['1']['2']['2']['2']['2']['4']['2'] = b'170'

	#printStars (decItem)

	encItem = blackboxprotobuf.encode_message(decItem, itemTypeOf)

	#replaced message
	mes2 = zlib.compress(encItem)

	decdata['4']['6']['3'] = mes2

	data2 = blackboxprotobuf.encode_message(decdata, typeof)
	#printStars(data2)

	with open("Reencoded_AppearSet.aptmpl", "wb") as f:
	f.write(data2)

	print ('done')
	import pyautogui
	import time
	import tkinter as tk
	from tkinter import messagebox
	from pynput import mouse
	from pynput import keyboard
	import math


	clicked_points = [] #start, step, end
	delay = 3

	def on_click(x, y, button, pressed):
	if pressed:
	#print(f"You clicked at ({x}, {y}) with {button}")
	clicked_points.append((x,y))
	# Stop after collecting 5 points (change as needed)
	if len(clicked_points) >= 3:
	#print("Captured 3 points:")
	for point in clicked_points:
	print(point)
	return False # Stop listening

	def on_press(key):
	try:
	global delay
	#print(f"Key pressed: {key.char}")
	delay = int(key.char)
	return False
	except AttributeError:
	print(f"Special key pressed: {key}")

	root = tk.Tk()
	root.withdraw() # Hide the main window
	root.attributes("-topmost", True) # Make sure it's on top

	tk.messagebox.showinfo("ScreenGrabber", f"Please click start,offset and end point")

	with mouse.Listener(on_click=on_click) as listener:
	listener.join()


	screenWidth, screenHeight = pyautogui.size() # Get the size of the primary monitor.
	print (screenWidth, screenHeight)


	#time.sleep(3) # Wait for the window to appear

	currentMouseX, currentMouseY = pyautogui.position()

	print (currentMouseX, currentMouseY)

	start = clicked_points[0]
	end = clicked_points[2]
	step = clicked_points[1][1]-clicked_points[0][1]
	count = math.ceil((end[1] - start[1])/step) + 1

	print (f"length {end[1] - start[1]}")
	print (f"step {step}")
	print (f"count {count}")

	tk.messagebox.showinfo("ScreenGrabber", f"Click OK and type the delay in seconds. Default is 3 seconds")


	with keyboard.Listener(on_press=on_press) as listener:
	listener.join()

	time.sleep(1)



	for i in range(0,count):
	pyautogui.moveTo(start[0], start[1] + i * step)
	pyautogui.click()
	time.sleep(delay) # Wait for the window to appear
	timestamp = time.time()
	local_time = time.localtime(timestamp)
	formatted_time = time.strftime("%Y-%m-%d %H:%M:%S", local_time)
	formatted_time = formatted_time.replace(':','_')
	screenshot = pyautogui.screenshot()
	screenshot.save(formatted_time + "_FBA.png")

	tk.messagebox.showinfo("ScreenGrabber", f"Done")
	import pandas as pd
	import yfinance as yf

	def adrp(df, length):
	ratio = df['High'] / df['Low']
	sma_ratio = ratio.rolling(length).mean()
	range_pct = round((sma_ratio - 1) * 100,2)
	return range_pct

	def check_price_streak(
	df,
	ticker,
	start_date,
	min_value,
	bought_price,
	target_value,
	):
	# Download historical data
	#df = yf.download(ticker,period="30d", interval="1d", progress=False, auto_adjust=True)

	a = adrp(df, 20)


	# Ensure datetime index
	df.index = pd.to_datetime(df.index)

	# Filter starting from the chosen date
	df = df.loc[start_date:]
	a = a.loc[start_date]
	print(f"ADR {round(a.item(),2)}%")

	risk = round(((bought_price/min_value) - 1) * 100,2)

	print (f"Risk {risk}%")

	if (risk > 0.66 * a.item()):
	print (f"Risk too high!!! Risk = {round(risk/a.item(),2)}")
	else:
	print (f"Risk < 0.66 x ADR. Acceptable Risk = {round(risk/a.item(),2)}")

	# Safely extract 'Close', 'High', and 'Low' prices as Series, handling MultiIndex if present
	close = None
	high = None
	low = None

	if isinstance(df.columns, pd.MultiIndex):
	close_col_tuple = ('Close', ticker)
	high_col_tuple = ('High', ticker)
	low_col_tuple = ('Low', ticker)

	# Attempt to get directly if columns exist as (ColumnType, Ticker)
	if close_col_tuple in df.columns:
	close = df[close_col_tuple]
	if high_col_tuple in df.columns:
	high = df[high_col_tuple]
	if low_col_tuple in df.columns:
	low = df[low_col_tuple]

	# Fallback if the MultiIndex structure isn't exactly ('ColumnType', Ticker)
	if close is None and 'Close' in df.columns and isinstance(df['Close'], pd.DataFrame) and ticker in df['Close'].columns:
	close = df['Close'][ticker]
	if high is None and 'High' in df.columns and isinstance(df['High'], pd.DataFrame) and ticker in df['High'].columns:
	high = df['High'][ticker]
	if low is None and 'Low' in df.columns and isinstance(df['Low'], pd.DataFrame) and ticker in df['Low'].columns:
	low = df['Low'][ticker]

	# Final fallback if still not found, and 'Close' / 'High' / 'Low' are top-level series
	if close is None and 'Close' in df.columns and isinstance(df['Close'], pd.Series):
	close = df['Close']
	if high is None and 'High' in df.columns and isinstance(df['High'], pd.Series):
	high = df['High']
	if low is None and 'Low' in df.columns and isinstance(df['Low'], pd.Series):
	low = df['Low']

	else:
	# Standard case: columns are flat, e.g., 'Close'
	close = df['Close']
	high = df['High']
	low = df['Low']

	# Check if we successfully extracted all series
	if close is None or high is None or low is None:
	raise ValueError(f"Could not reliably extract 'Close', 'High', or 'Low' price for {ticker} from DataFrame with unexpected column structure.")


	# Ensure 'close', 'high', and 'low' are Series. Squeeze if they are single-column DataFrames.
	if isinstance(close, pd.DataFrame) and len(close.columns) == 1:
	close = close.squeeze()
	elif not isinstance(close, pd.Series):
	raise ValueError("Expected 'close' to be a pandas Series but got a different type after extraction.")

	if isinstance(high, pd.DataFrame) and len(high.columns) == 1:
	high = high.squeeze()
	elif not isinstance(high, pd.Series):
	raise ValueError("Expected 'high' to be a pandas Series but got a different type after extraction.")

	if isinstance(low, pd.DataFrame) and len(low.columns) == 1:
	low = low.squeeze()
	elif not isinstance(low, pd.Series):
	raise ValueError("Expected 'low' to be a pandas Series but got a different type after extraction.")

	n = len(close) # Assuming 'high' and 'low' have the same length and index

	if n < 2:
	return {
	"days": 0,
	"last_date": None,
	"last_price": None,
	"roi": "N/A",
	"reason": "Not enough data after start date"
	}

	streak = 2

	# Start from the day after the chosen start date
	for i in range(1, n):
	current_close_price = close.iloc[i].item()
	current_high_price = high.iloc[i].item()
	current_low_price = low.iloc[i].item()



	# Condition 1: Intraday low dropped below minimum value (most aggressive stop-loss)
	if current_low_price <= min_value:
	final_price_for_roi = min_value # Assume sold at min_value
	roi = f"Gain {round((final_price_for_roi - bought_price)/bought_price*100,2)}%"
	return {
	"days": streak,
	"last_date": close.index[i], # Date when the low occurred
	"last_price": current_low_price, # The actual low price that triggered
	"roi": roi,
	"reason": f"Intraday low dropped below minimum ({min_value})"
	}

	# Condition 2: Closing price dropped below minimum value
	if current_close_price <= min_value:
	final_price_for_roi = min_value # Assume sold at min_value
	roi = f"Gain {round((final_price_for_roi - bought_price)/bought_price*100,2)}%"
	return {
	"days": streak,
	"last_date": close.index[i], # Date when the close occurred
	"last_price": current_close_price, # The actual close price that triggered
	"roi": roi,
	"reason": f"Closing price dropped below minimum ({min_value})"
	}

	# Condition 3: Intraday high reached or exceeded target value
	if current_high_price >= target_value:
	final_price_for_roi = target_value # Assume sold at target price
	roi = f"Gain {round((final_price_for_roi - bought_price)/bought_price*100,2)}%"
	return {
	"days": streak,
	"last_date": close.index[i], # Date when the high occurred
	"last_price": current_high_price, # The actual high price that triggered
	"roi": roi,
	"reason": f"Intraday high reached or exceeded target ({target_value})"
	}

	# Condition 4: Closing price reached or exceeded target value (if not already met by high)
	if current_close_price >= target_value:
	final_price_for_roi = target_value # Assume sold at target price
	roi = f"Gain {round((final_price_for_roi - bought_price)/bought_price*100,2)}%"
	return {
	"days": streak,
	"last_date": close.index[i], # Date when the close occurred
	"last_price": current_close_price, # The actual close price that triggered
	"roi": roi,
	"reason": f"Closing price reached or exceeded target ({target_value})"
	}

	streak += 1

	# If we never broke any condition by the end of the data
	last_price_at_end = close.iloc[-1].item()
	roi_at_end = f"Gain {round((last_price_at_end - bought_price)/bought_price*100,2)}%"
	return {
	"days": streak,
	"last_date": close.index[-1],
	"last_price": last_price_at_end,
	"roi": roi_at_end,
	"reason": "Reached end of data"
	}


	### INPUTS ###
	t = "NKTR"
	start_date = "2025-11-11"
	### END ###
	data = yf.download(t, period="30d", interval="1d", progress=False, auto_adjust=True)
	#print (data)
	min_value = data.loc[start_date]['Low'].item()
	bought_price = data.loc[start_date]['High'].item()
	target_value = round(bought_price/min_value*bought_price,2)
	print(f"Bought Price ${round(bought_price,2)}")
	print(f"Bottom Price ${round(min_value,2)}")
	print(f"Target Price ${target_value}")
	print(f"Target Gain {round(((target_value/bought_price)-1)*100,2)}%")
	result = check_price_streak(
	df =data,
	ticker=t,
	start_date=start_date,
	min_value=min_value,
	bought_price=bought_price,
	target_value=target_value,
	)
	print(result)
	import pandas as pd
	import yfinance as yf
	import pytz
	from datetime import datetime, time, timedelta

	# ===========================
	# Helper Functions
	# ===========================
	def adrp(df, length):
	"""
	df: pandas DataFrame containing df['High'] and df['Low']
	length: SMA length (adrp_len)
	"""
	ratio = df['High'] / df['Low']
	sma_ratio = ratio.rolling(length).mean()
	adrp_percent = 100 * (sma_ratio - 1)
	return adrp_percent

	def TestDate(df):
	df = df.copy()
	df.columns = [col[0] if isinstance(col, tuple) else col for col in df.columns]
	df["MA6"] = df["Close"].rolling(window=6, min_periods=6).mean()

	df_date = (df.loc["2025-11-07"])

	print (df_date)

	print('condition2', (df_date["High"] - df_date["Close"]) / df_date["High"] )# Close near high (within 0.5%)
	print('condition3', (df_date["Low"] / df_date["Close"]) )# Low is within 7% of close
	print ('condition4a', (abs(df_date["Open"] - df_date["MA6"]) / df_date["MA6"]))
	print ('condition4b', (abs(df_date["Close"] - df_date["MA6"]) / df_date["MA6"]))

	condition1 = df_date["Open"] < df_date["Close"] # Green candle
	condition2 = (df_date["High"] - df_date["Close"]) / df_date["High"] < 0.0055 # Close near high (within 0.5%)
	condition3 = (df_date["Low"] / df_date["Close"]) > 0.93 # Low is within 7% of close
	condition4 = (
	(abs(df_date["Open"] - df_date["MA6"]) / df_date["MA6"] < 0.018) \|
	(abs(df_date["Close"] - df_date["MA6"]) / df_date["MA6"] < 0.018)
	)

	print (condition1, condition2, condition3, condition4)

	def GetTriggerBar(df):
	#TestDate(df)

	df = df.copy()
	df.columns = [col[0] if isinstance(col, tuple) else col for col in df.columns]
	# Compute 6-day moving average
	df["MA6"] = df["Close"].rolling(window=6, min_periods=6).mean()

	# Filter conditions:
	# 1. open < close (green candle)
	# 2. close is almost equal to high (close/high close to 1.0, difference < 1%)
	# 3. low/close < 3% (wick at bottom is small)

	condition1 = df["Open"] < df["Close"] # Green candle
	condition2 = (df["High"] - df["Close"]) / df["High"] < 0.0055 # Close near high (within 0.5%)
	condition3 = (df["Low"] / df["Close"]) > 0.93 # Low is within 7% of close
	condition4 = (
	(abs(df["Open"] - df["MA6"]) / df["MA6"] < 0.018) \|
	(abs(df["Close"] - df["MA6"]) / df["MA6"] < 0.018)
	)
	# Combine all conditions
	mask = condition1 & condition2 & condition3 & condition4


	# Print matching dates and values
	matching_dates = df[mask]
	cleaned_df = matching_dates.dropna(how='all')

	# Define the cutoff date (10 days ago)
	cutoff_date = datetime.now() - timedelta(days=10)

	# Filter only rows from the last 10 calendar days
	recent_matches = cleaned_df[cleaned_df.index >= cutoff_date]

	#add ADR check
	#add ma6 in uptrend

	#above 10 days MA6 cleaned_df["Close"].iloc[-1] > cleaned_df["MA6"].iloc[-1]:
	if not cleaned_df.empty and not recent_matches.empty:
	print (F'********************************{t}**************************')
	# print("Dates matching criteria (Open < Close, Close ≈ High, Low/Close > 93%):")
	print(recent_matches[["Open", "Close", "High", "Low","Volume","MA6"]])

	# ===========================
	# Main Logic
	# ===========================
	# // Select all elements with the class
	# let elements = document.querySelectorAll('.tickerNameBox-GrtoTeat');

	"""
	let elements = document.querySelectorAll('.tickerNameBox-GrtoTeat');
	let titles = Array.from(elements).map(el => el.title);
	copy(titles.join("\n")); // copies directly to clipboard
	console.log("Copied to clipboard!");
	"""
	clipboard_text = """
	RLMD − Relmada Therapeutics, Inc.
	IMMX − Immix Biopharma, Inc.
	IMTX − Immatics N.V.
	RPID − Rapid Micro Biosystems, Inc.
	PEPG − PepGen Inc.
	NMRA − Neumora Therapeutics, Inc.
	GALT − Galectin Therapeutics Inc.
	OLMA − Olema Pharmaceuticals, Inc.
	MOD − Modine Manufacturing Company
	TNGX − Tango Therapeutics, Inc.
	PLPC − Preformed Line Products Company
	LMND − Lemonade, Inc.
	OMER − Omeros Corporation
	OPXS − Optex Systems Holdings, Inc.
	LUMN − Lumen Technologies, Inc.
	UFPT − UFP Technologies, Inc.
	ARDX − Ardelyx, Inc.
	COCO − The Vita Coco Company, Inc.
	OPEN − Opendoor Technologies Inc
	LASR − nLIGHT, Inc.
	VHC − VirnetX Holding Corp
	TERN − Terns Pharmaceuticals, Inc.
	ESPR − Esperion Therapeutics, Inc.
	SMHI − SEACOR Marine Holdings Inc.
	ENTA − Enanta Pharmaceuticals, Inc.
	LAES − SEALSQ Corp
	REKR − Rekor Systems, Inc.
	PGEN − Precigen, Inc.
	OESX − Orion Energy Systems, Inc.
	AIP − Arteris, Inc.
	ANIX − Anixa Biosciences, Inc.
	LQDA − Liquidia Corporation
	TYGO − Tigo Energy, Inc.
	"""

	#CHECK: KSS 07/11 NOT SHOWING, TTI 07/11 NOT SHOWING
	#MONITOR: TRVI, TTI, WDC, LXU, SWAG, OLMA


	tickers = [line.strip().split(' − ')[0] for line in clipboard_text.splitlines() if line.strip()]
	# tickers = ["TTI"]
	print ("Tickers to analyze:", tickers)

	## TRIGGER BAR <3%

	for t in tickers:
	df = yf.download(t, period="30d", interval="1d", progress=False, auto_adjust=True)
	GetTriggerBar(df)