app.py

import streamlit as st
from io import StringIO
import pandas as pd


from returns_gen import returns_set
#from plot_simulations import plot_sims,histogram
from maximum_dd import max_dd
import numpy as np
st. set_page_config(page_title='Monte Carlo Simulator ',layout="centered")

st.header('Monte Carlo Simulator for 10000 iterations')

from matplotlib import pyplot as plt


def plot_sims(sims_list):
    global generated_plots

    # sims_list=sims_list[:10]
    fig, ax = plt.subplots()
    for sl in sims_list:
        ax.plot(sl)
    number_of_sims=len(sims_list)
    ax.set_title(f'Monte Carlo Paths of Nifty returns for {number_of_sims} simulations')
    ax.set_ylabel('returns %')
    ax.set_xlabel('count')
    st.pyplot(fig)
   
def histogram(lis):
    lis.sort()
    print(min(lis),max(lis))
    print(int(min(lis)),int(max(lis)))

    bin_width=0.5
    max_val=int(max(lis))+1
    min_val=int(min(lis))-1
    bins=[min_val+k*bin_width for k in range(int((max_val-min_val)/bin_width))]
    fig,ax=plt.subplots()
    ax.hist(lis,bins=bins,align='left')
    #ax.xlim(max_val,min_val)
    ax.axis(xmin=min_val,xmax=max_val)
    ax.set_title('Drawdown from Monte Carlo iterations')
    ax.set_ylabel('count')
    ax.set_xlabel('Maxx DD %')
    st.pyplot(fig)

def clean_returns(ret_lis):
    return [float(str(k).replace(',','').replace('%','').replace(' ','')) for k in returns_list]

uploaded_file=st.file_uploader('DataFile', type=['csv'])
if uploaded_file:
    stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
    df=pd.read_csv(stringio)
   
    df_cols=df.columns
    
    default_col=0
    for i in range(len(df_cols)):
        dcol=df_cols[i]
        if 'return' in dcol.lower():
            default_col=i
            break
    returns_column=st.selectbox('returns column', df_cols,index=default_col)
  
   
    returns_list=df[returns_column].tolist()
    returns_list=clean_returns(returns_list)


    returns=returns_set(returns_list)


    cum_rets_dd=[max_dd(k) for k in returns]
    cum_rets=[k[0] for k in cum_rets_dd]
    dd_list=[k[1] for k in cum_rets_dd]
    percentile_95=np.percentile(dd_list,95)
    st.write(f'95 percentile Max DD is {percentile_95} %')
    plot_sims(cum_rets)
    histogram(dd_list)