Spaces:
Running
Running
File size: 7,724 Bytes
acd39d4 6e5474c 4d2bcc0 acd39d4 53c15f8 6e5474c 53c15f8 6735566 5e9b196 6735566 6e5474c d751ea4 6e5474c d751ea4 4d2bcc0 6e5474c 6735566 6e5474c 6735566 6e5474c 3bb6db2 6735566 5e9b196 6735566 5e9b196 6735566 3bb6db2 53c15f8 6e5474c 3bb6db2 6e5474c d50a99d 6e5474c 5e9b196 6735566 5e9b196 6735566 6e5474c 53c15f8 d76be43 53c15f8 d76be43 3bb6db2 c141d0b 6e5474c 3bb6db2 6e5474c 3bb6db2 6e5474c 3bb6db2 6e5474c 3bb6db2 6e5474c 6735566 6e5474c 6735566 3bb6db2 6735566 3bb6db2 5e9b196 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 |
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"<b>Comp. Name: </b> {si['longName']}, {si['city']}, {si.get('state', '')} {si['country']} <br>"
f"<b>Web site: </b> <a href=\"{si['website']}\">{si['website']}</a> <br>"
f"<b>Stock Price: </b> {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"<b>Current Price: </b> {stock_info.info['currentPrice']} <br>"
f"<b>EOD Close Price: </b> <span style='color:{color};'> {eod_forecast_price:.2f} </span>   <span style='color:{color};'> {mark}{eod_forecast_p}% </span> ")
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"<b>EOD Tomorrow Close Price: </b> <span style='color:{color};'> {eod_forecast_price:.2f} </span>   <span style='color:{color};'> {mark}{eod_forecast_p}% </span> ")
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(
"""
<style>
.appview-container .main .block-container {{
padding-top: {padding_top}rem;
padding-bottom: {padding_bottom}rem;
}}
</style>""".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"<H4>{long_name}</H4>", 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"<H4>Market Sentiment</H4>", unsafe_allow_html=True)
df = print_sentiment(stock_info)
st.write(f"<H6>Sentiment summery</H6>", unsafe_allow_html=True)
print_sentiment_summery(df)
st.write(f"<H4>Forecasting</H4>", 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)
|