main.py

from modules import *

st.set_page_config(layout="wide")


col1, col2= st.columns(2)

with col1:
    st.markdown("# Stock Data")
    #get file names from data/
    def get_filenames(directory):
        # List all files in the directory
        files = os.listdir(directory)
        
        # Filter and remove the .csv extension
        filenames = [os.path.splitext(file)[0] for file in files if file.endswith('.csv')]
        
        return filenames

    directory = './data'
    filenames = get_filenames(directory)

    #select box with stock names from filenames
    stock = st.selectbox(
        "Please select the stock for quant analysis:",
        (filenames),
    )

    def get_date(stock):
        csv_loc = f'./data/{stock}.csv'
        df = pd.read_csv(csv_loc, sep=',', header=0)
        df['Date'] = pd.to_datetime(df['Date'])
        min_date = df['Date'].min().date()
        max_date = df['Date'].max().date()
        return min_date, max_date

    min_date, max_date = get_date(stock)
    #select data range to display on chart/ perform operations with selected range.. default always 0% 100%
    date_range = st.slider(
        "Select date range: ",
        value=(
                date(min_date.year, min_date.month, min_date.day), 
                date(max_date.year, max_date.month, max_date.day)
            ),
        format="YYYY/MM/DD",
    )

    #table with usefull information, avg annual rate, std deviation.. check porfolio analyser

    def stock_data():
        csv_loc = f'./data/{stock}.csv'
        df = pd.read_csv(csv_loc, sep=',', header=0)
        return df.set_index("Date")

    csv_loc = './data/QQQ.csv'

    # Specify the date range
    start_date = f'{date_range[0]}'
    end_date = f'{date_range[1]}'

    # Function to filter rows based on date range
    def filter_date_range(df, start_date, end_date):
        return df[(df.index >= start_date) & (df.index <= end_date)]

    #####################################
    # Optimised Stop Loss
    #####################################
    def OSL(df, start_date, end_date):
        csv_loc = f'./data/{stock}.csv'


    # Read the CSV file and parse dates
    df = pd.read_csv(
        csv_loc,
        sep=',',
        parse_dates=['Date'],  # Replace 'Date' with the name of your date column
        header=0,
        skiprows=0
    )
    df_filtered = df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]
    st.line_chart(df_filtered, x="Date", y="Close")
    
    table_column = ["ATR","return","annualize average return"]
    df = pd.DataFrame(
    np.random.randn(1, len(table_column)), columns=table_column
    )
    st.table(df)

#####################################
# RANDOM TRADE
#####################################
with col2:
    st.markdown("# Probability calculator")

    col1, col2, col3 = st.columns(3)
    with col1:
        amount = st.number_input("Starting \$$$")
    with col2:
        stop_loss = st.number_input("S/L pips")
    with col3:
        stop_loss = st.number_input("T/P pips")

    if st.button("Enter trading sequence", type="primary"):
        col1, col2, col3, col4, col5 = st.columns(5)
        with col1:
            "Total Trades"
        with col2:
            "Winning Trades"
        with col3:
            "Loosing Trades"
        with col4:
            "Max trade time (Days)"
        with col5:
            "Min trade time (Days)"

    else:
        st.write("Goodbye")