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import os
import json
from openai import OpenAI
import openai
import pandas as pd
import matplotlib.pyplot as plt
import yfinance as yf
import streamlit as st
from dotenv import load_dotenv
load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
client = OpenAI()
def get_stock_price(ticker):
return str(yf.Ticker(ticker).history(period = '1y').iloc[-1].Close)
def calculate_SMA(ticker, window):
data = yf.Ticker(ticker).history(period = '1y').Close
return str(data.rolling(window = window).mean().iloc[-1])
def calculate_EMA(ticker, window):
data = yf.Ticker(ticker).history(period = '1y').Close
return str(data.ewm(span = window, adjust = False).mean().iloc[-1])
def calculate_RSI(ticker):
data = yf.Ticker(ticker).history(period = '1y').Close
delta = data.diff()
up = delta.clip(lower = 0)
down = -1 * delta.clip(upper = 0)
ema_up = up.ewm(com = 14-1, adjust = False).mean()
ema_down = down.ewm(com = 14 - 1, adjust = False).mean()
rs = ema_up / ema_down
return str(100 - (100 / (1 + rs)).iloc[-1])
def calculate_MACD(ticker):
data = yf.Ticker(ticker).history(period = '1y').Close
short_EMA = data.ewm(span = 12, adjust = False).mean()
long_EMA = data.ewm(span = 26, adjust = False).mean()
MACD = short_EMA - long_EMA
signal = MACD.ewm(span = 9, adjust = False).mean()
MACD_histogram = MACD - signal
return f'{MACD[-1]}, {signal[-1]}, {MACD_histogram[-1]}'
def plot_stock_price(ticker):
data = yf.Ticker(ticker).history(period = '1y')
plt.figure(figsize=(10, 5))
plt.plot(data.index, data.Close)
plt.title(f"{ticker} Stock Price over last year")
plt.xlabel('Date')
plt.ylabel('Stock Price ($)')
plt.grid(True)
plt.savefig('stock.png')
plt.close()
functions = [
{
'name': 'get_stock_price',
'description': 'Gets the latest stock price given the ticker symbol of company',
'parameters':{
'type': 'object',
'properties': {
'ticker':{
'type': 'string',
'description': 'The stock ticker symbol for a company (for example AAPL for Apple)'
}
},
'required': ['ticker']
}
},
{
'name': 'calculate_SMA',
'description': 'Calculate the simple moving average for a given stock ticker and a window',
'parameters':{
'type': 'object',
'properties':{
'ticker':{
'type': 'string',
'description': 'The stock ticker symbol for a company (for example AAPL for Apple)'
},
'window':{
'type': 'integer',
'description': 'The timeframe to consider when calculating the SMA'
},
},
'required':['ticker', 'window']
},
},
{
'name': 'calculate_EMA',
'description': 'Calculate the exponential moving average for a given stock ticker and a window',
'parameters':{
'type': 'object',
'properties':{
'ticker':{
'type': 'string',
'description': 'The stock ticker symbol for a company (for example AAPL for Apple)'
},
'window':{
'type': 'integer',
'description': 'The timeframe to consider when calculating the EMA'
},
},
'required':['ticker', 'window']
},
},
{
'name': 'calculate_RSI',
'description': 'Calcuate the RSI for a given stock ticker',
'parameters':{
'type': 'object',
'properties': {
'ticker': {
'type': 'string',
'description': 'The stock ticker symbol for a company (for example AAPL for Apple)'
}
},
'required': ['ticker']
},
},
{
'name': 'calculate_MACD',
'description': 'Cacluate the MACD for a given stock ticker.',
'parameters':{
'type': 'object',
'properties':{
'ticker':{
'type': 'string',
'description': 'The stock ticker symbol for a company (for example AAPL for Apple)'
},
},
'required': ['ticker']
},
},
{
'name': 'plot_stock_price',
'description': 'Plot the stock price for the last year given the ticker symbol of a company',
'parameters':{
'type': 'object',
'properties':{
'ticker':{
'type':'string',
'description': 'The stock ticker symbol for a company (for example AAPL for Apple)'
},
},
'required': ['ticker']
},
},
]
available_functions = {
'get_stock_price': get_stock_price,
'calculate_SMA': calculate_SMA,
'calculate_EMA': calculate_EMA,
'calculate_RSI': calculate_RSI,
'calculate_MACD': calculate_MACD,
'plot_stock_price': plot_stock_price
}
if 'messages' not in st.session_state:
st.session_state['messages'] = []
st.title("Financial Stock Assistant")
user_input = st.text_input('Your Input: ')
if user_input:
try:
st.session_state['messages'].append({'role': 'user', 'content': f'{user_input}'})
response = openai.chat.completions.create(
model = 'gpt-3.5-turbo-0125',
messages = st.session_state['messages'],
functions = functions,
function_call = 'auto'
)
response_message = response.choices[0].message
if response_message.function_call:
function_name = response_message.function_call.name
function_args = json.loads(response_message.function_call.arguments)
if function_name in ['get_stock_price', 'calculate_RSI', 'calculate_MACD', 'plot_stock_price']:
args_dict = {'ticker': function_args['ticker']}
elif function_name in ['calculate_SMA', 'calculate_EMA']:
args_dict = {'ticker': function_args['ticker'], 'window': function_args['window']}
function_to_call = available_functions[function_name]
function_response = function_to_call(**args_dict)
if function_name == 'plot_stock_price':
st.image('stock.png')
else:
st.session_state['messages'].append(response_message)
st.session_state['messages'].append(
{
'role': 'function',
'name': function_name,
'content': function_response
}
)
second_response = openai.chat.completions.create(
model = 'gpt-3.5-turbo-0613',
messages = st.session_state['messages']
)
st.write(second_response.choices[0].message.content)
st.session_state['messages'].append({'role': 'assistant', 'content': second_response.choices[0].message.content})
else:
st.write(response_message.content)
st.session_state['messages'].append({'role': 'assistant', 'content': response_message.content})
except :
st.write("Stock data not found!") |