DevOps_project_2/app.py at main · Danishain/DevOps_project_2 · GitHub

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import streamlit as st
import fsspec
import boto3
import botocore
import pandas as pd
import numpy as np

# Connect to S3
s3 = boto3.client('s3')

st.set_page_config(page_title="My S3 App", page_icon=":guardsman:", layout="wide")

delta = st.slider('Select differnce points from market value', -50, 50, 0, 10)

open_time = st.selectbox('Choose Opening time', [10, 11, 12])

close_time = st.selectbox('Choose Closing time', [13, 14, 15, 16])

months = st.multiselect(
    'Select Months',
    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], )

all_options = st.checkbox("Select whole Year", 0)
if all_options:
    months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

# Get parquet file from S3
file = 'trades2021.parquet'

# Read data from S3
df = pd.read_parquet(f"s3://schiff-trades-2021/{file}")

# Create timestamp for date and time columns
df['timestamp'] = df['date'] + pd.to_timedelta(df['time'])
df['month'] = df['timestamp'].dt.month


def Day_Night_Result_table(df, buy_morning=True, open_time=open_time, close_time=close_time,
                           delta=delta, months=months):
    months = [months] if type(months) == int else months

    open_trades = df[(df['timestamp'].dt.hour < open_time) &
                     (df['diff_mimush'] == delta) &
                     (df['timestamp'].dt.month.isin(months))]

    close_trades = df[(df['timestamp'].dt.hour > close_time) &
                      (df['diff_mimush'] == delta) &
                      (df['timestamp'].dt.month.isin(months))]

    full_trades = pd.concat([open_trades, close_trades], ignore_index=True).sort_values(
        by=['timestamp', 'mispar_hoze']).reset_index(drop=True)

    grouped = full_trades.groupby(['date', 'mispar_hoze'])
    first_trade = grouped.first().reset_index()
    last_trade = grouped.last().reset_index()

    if buy_morning:
        first_trade['trade_type'] = 'buy'
        last_trade['trade_type'] = 'sell'
    else:
        first_trade['trade_type'] = 'sell'
        last_trade['trade_type'] = 'buy'

    df_final = pd.concat([first_trade, last_trade], ignore_index=True)
    df_final['buy_sell'] = np.where(df_final.trade_type == 'buy', -df_final['p'], df_final['p'])
    # number_of_trades = df_final.shape[0]
    # result= df_final.groupby(['call','month'])['buy_sell'].sum().reset_index().rename(columns={'call': 'option_type'})
    # result['option_type'] = result['option_type'].map({0.0: 'Put', 1.0: 'Call'})

    return (df_final)


def Output_Table(df):
    df_final = Day_Night_Result_table(df).rename(columns={'call': 'option_type'})

    result = df_final.groupby(['option_type', 'month'])['buy_sell'].agg(['sum', 'count']).reset_index()

    result['option_type'] = result['option_type'].map({0.0: 'Put', 1.0: 'Call'})
    result['month'] = result['month'].astype(int)
    result['open_time'] = open_time
    result['close_time'] = close_time
    result['diff'] = delta
    return result


# def calculate_mean(df):
#     return df.mean()

# def calculate_sum(data):
#     return df.sum()


st.title("Options Data")
if st.button("TEST STRATEGY"):
    results = Output_Table(df)
    st.write("Result:", results)

st.title("Save to S3")
if st.button("Save Results"):
    results = Output_Table(df)
    results.to_csv(f"diff{delta}open{open_time}close{close_time}.csv")
    s3.upload_file(f"diff{delta}open{open_time}close{close_time}.csv", "strategy-result-1",
                   f"diff{delta}open{open_time}close{close_time}.csv")
    st.write("Results Saved!")