import json
import streamlit as st
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
import requests
url_stocks = "https://financialmodelingprep.com/api/v3/stock/list?apikey="
url_sentiment = "https://yaakovy-fin-proj-docker.hf.space/ticker/"
url_timeGpt = "https://ofirmatzlawi-fin-proj-docker-1.hf.space/ticker/"
url_forecast_eod = "https://yaakovy-lasthourforcast.hf.space/ticker/"
url_forecast_eod24 = "https://ofirmatzlawi-fin-proj-docker-2.hf.space/ticker/"
def get_max_sentiment(row):
if row["sentiment_score"] > 0.05: # Threshold for positive sentiment
return "positive"
elif row["sentiment_score"] < -0.05: # Threshold for negative sentiment
return "negative"
else:
return "neutral"
def get_sentiment_data(stock_info):
symbol = stock_info.info['symbol']
url_sentiment_with_ticker = f"{url_sentiment}{symbol}"
response = requests.get(url_sentiment_with_ticker)
if response.status_code == 200:
json_data = json.loads(response.json())
df = pd.DataFrame(json_data)
df['sentiment'] = df.apply(get_max_sentiment, axis=1)
df = df.drop(['neg', 'neu', 'pos', 'sentiment_score'], axis=1)
return df
else:
return
def print_sentiment(stock_info):
df = get_sentiment_data(stock_info)
#st.write("Market Sentiment")
st.dataframe(df, hide_index =True )
return df
def get_eod_forecast24(stock_info):
symbol = stock_info.info['symbol']
url_forecast_eod_with_ticker = f"{url_forecast_eod24}{symbol}"
response = requests.get(url_forecast_eod_with_ticker)
if response.status_code == 200:
eod_forecast = json.loads(response.json())
#st.write(json_data)
#eod_forecast = json_data["latest_prediction"]
return eod_forecast
else:
return
def get_eod_forecast(stock_info):
symbol = stock_info.info['symbol']
url_forecast_eod_with_ticker = f"{url_forecast_eod}{symbol}"
response = requests.get(url_forecast_eod_with_ticker)
if response.status_code == 200:
json_data = json.loads(response.json())
eod_forecast = json_data["latest_prediction"]
return eod_forecast
else:
return
def print_sentiment_summery(df) :
column_name = "sentiment"
category_counts = df[column_name].value_counts()
df_sentiment = pd.DataFrame({
"Sentiment": category_counts.index,
"Count": category_counts.values
})
st.dataframe(df_sentiment, hide_index =True )
return df_sentiment
def print_stock_info(stock_info):
stock_info_html = get_stock_info_from_html(stock_info.info)
st.write(stock_info_html, unsafe_allow_html=True)
plot_graph(stock_info)
col1, col2 = st.columns([0.8, 0.2])
with col1:
st.pyplot(plt)
with col2:
tf = st.radio(
"Select Time Frame",
["1Y", "3Y", "5Y", "10Y"], index=2,
key="chart_time_frame",
)
def get_stock_info_from_html(stock_info):
si = stock_info
text = (f"Comp. Name: {si['longName']}, {si['city']}, {si.get('state', '')} {si['country']}
"
f"Web site: {si['website']}
"
f"Stock Price: {si['currentPrice']} {str(si['financialCurrency'])}")
return text
def get_forecast_html(stock_info):
currentPrice = stock_info.info['currentPrice']
eod_forecast = get_eod_forecast(stock_info)
eod_forecast_price = currentPrice * (1 + eod_forecast/100)
color = 'red' if eod_forecast < 0 else 'green'
mark = '+' if eod_forecast >= 0 else '-'
eod_forecast_p = abs(round(eod_forecast, 2))
html = (f"Current Price: {stock_info.info['currentPrice']}
"
f"EOD Close Price: {eod_forecast_price:.2f} {mark}{eod_forecast_p}% ")
return html
def get_forecast_html24(stock_info):
currentPrice = stock_info.info['currentPrice']
eod_forecast = get_eod_forecast24(stock_info)
eod_forecast_price = currentPrice * (1 + eod_forecast/100)
color = 'red' if eod_forecast < 0 else 'green'
mark = '+' if eod_forecast >= 0 else '-'
eod_forecast_p = abs(round(eod_forecast, 2))
html = (f"EOD Tomorrow Close Price: {eod_forecast_price:.2f} {mark}{eod_forecast_p}% ")
return html
def plot_graph(stock_info):
period = st.session_state.chart_time_frame or "5Y"
history = stock_info.history(period=period)
name = stock_info.info['longName']
plt.plot(history['Close'])
plt.xlabel('Date')
plt.ylabel('Price')
plt.title(f"{name} Stock Price")
return plt
def print_timeGpt(stock_info):
symbol = stock_info.info['symbol']
url_timeGpt_with_ticker = f"{url_timeGpt}{symbol}"
response = requests.get(url_timeGpt_with_ticker)
if response.status_code == 200:
json_data = json.loads(response.json())
#st.write(json_data)
json_data = json.loads(response.json())
data = json_data["data"]
converted_data = []
for row in data:
converted_data.append({"Date": row[0], "TimeGPT": row[1]})
df = pd.DataFrame(converted_data)
st.dataframe(df)
return df
else:
return
st.set_page_config(page_title="Senty Sense")
st.markdown(
"""
""".format(
padding_top=1, padding_bottom=1
),
unsafe_allow_html=True,
)
st.title('_SentySense_') #PriceProphet, Sentyment, Trendsetter Bullseye
par1 = "Our stock market platform gives you real-time data, historical insights, and in-depth news to help you make informed investment decisions."
st.write(par1, unsafe_allow_html=True)
if 'chart_time_frame' not in st.session_state:
st.session_state['chart_time_frame'] = '5Y'
if 'data_available' not in st.session_state:
st.session_state['data_available'] = False
option = 'Stocks' #st.selectbox("select", ["", "Currencies", "Stocks"], placeholder="Choose an option", label_visibility = "hidden")
if option == "Currencies":
input_text = "Enter currency pair"
else:
input_text = "Enter stock symbol"
text_box: str = None
btn_get_data = None
if option:
text_box = st.text_input(input_text)
with st.spinner('Wait for it...'):
if text_box:
ticker = text_box.upper()
try:
stock_info = yf.Ticker(ticker)
long_name = stock_info.info['longName']
st.write(f"{long_name}
", unsafe_allow_html=True)
except:
st.error('Ticker not found', icon="🚨")
st.session_state['data_available'] = False
else:
st.session_state['data_available'] = True
print_stock_info(stock_info)
st.write(f"Market Sentiment
", unsafe_allow_html=True)
df = print_sentiment(stock_info)
st.write(f"Sentiment summery
", unsafe_allow_html=True)
print_sentiment_summery(df)
st.write(f"Forecasting
", unsafe_allow_html=True)
st.write(get_forecast_html(stock_info), unsafe_allow_html=True)
st.write(get_forecast_html24(stock_info), unsafe_allow_html=True)