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analytics.py
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analytics.py
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import streamlit as st
import yfinance as yf
import pandas as pd
import requests
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import skew, kurtosis, norm, jarque_bera,linregress
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np
from scipy.stats.mstats import gmean
from prompts import format_stats_for_prompt, generate_ai_response,generate_anomaly_analytics_prompt,generate_ai_response_anomaly
from sklearn.ensemble import IsolationForest
from collections import defaultdict
import calendar
from datetime import datetime
# Function to fetch NEAR Blocks API data
def fetch_near_blocks_stats():
url = "https://api.nearblocks.io/v1/stats"
response = requests.get(url)
if response.status_code == 200:
data = response.json()
return data
else:
st.error("Failed to fetch NEAR Blocks API data.")
return {}
# Function to fetch NEAR-USD data
def get_near_data(start_date, end_date):
near = yf.Ticker("NEAR-USD")
return near.history(start=start_date, end=end_date)
# Function to display basic data and plots
def display_basic_data(df):
st.subheader("Basic NEAR-USD analysis")
st.write(df.describe()) # Display basic statistics
st.line_chart(df['Close']) # Line chart for closing prices
# Function for statistical analysis
def statistical_analysis(df):
st.subheader("Statistical Analysis")
returns = df['Close'].pct_change().dropna()
col1, col2 = st.columns(2)
with col1:
st.markdown(f"""
<div style="background-color:#f0f2f6;padding:10px;border-radius:10px;">
<h4 style="color:#333;">Mean:</h4>
<p style="color:red;">{returns.mean()}</p>
<h4 style="color:#333;">Median:</h4>
<p style="color:red;">{returns.median()}</p>
</div>
""", unsafe_allow_html=True)
with col2:
st.markdown(f"""
<div style="background-color:#f0f2f6;padding:10px;border-radius:10px;">
<h4 style="color:#333;">Min:</h4>
<p style="color:red;">{returns.min()}</p>
<h4 style="color:#333;">Max:</h4>
<p style="color:red;">{returns.max()}</p>
</div>
""", unsafe_allow_html=True)
st.markdown(f"""
<div style="background-color:#e8eaf6;padding:10px;border-radius:10px;">
<h4 style="color:#333;">Standard Deviation:</h4>
<p style="color:red;">{returns.std()}</p>
</div>
""", unsafe_allow_html=True)
# Histogram with normal distribution fit
plt.figure(figsize=(10, 6))
sns.histplot(returns, kde=True, stat="density", linewidth=0)
mu, std = norm.fit(returns)
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)
plt.plot(x, p, 'k', linewidth=2)
title = f"Fit results: mu = {mu:.2f}, std = {std:.2f}"
plt.title("Normal Density Function")
st.pyplot(plt)
plt.close()
# Function for distribution fitting
def distribution_fitting(returns):
sns.distplot(returns, fit=norm, kde=False)
plt.title("Normal Distribution Fit")
st.pyplot(plt)
plt.close()
# Function for stock price predictions
def stock_price_predictions(df):
st.subheader("Stock Price Predictions & Accuracy Score")
# Simplified prediction model using closing prices
X = df[['Open']]
y = df['Close']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)
score = model.score(X_test, y_test)
st.markdown(f"""
<div style="background-color:#e8eaf6;padding:10px;border-radius:10px;">
<h4 style="color:#333;">Model Accuracy Score:</h4>
<p style="color:red;">{score:.2f}</p>
</div>
""", unsafe_allow_html=True)
return score
# Function for Value at Risk
def value_at_risk(df):
st.subheader("Value at Risk (VaR)")
returns = df['Close'].pct_change().dropna()
st.bar_chart(returns)
st.markdown(f"""
<div style="background-color:#f0f2f6;padding:10px;border-radius:10px;margin-bottom:10px;">
<h4 style="color:#333;">Standard deviation:</h4>
<p style="color:red;">{returns.std():.2f}</p>
</div>
<div style="background-color:#f0f2f6;padding:10px;border-radius:10px;">
<h4 style="color:#333;">Inter-Quantile range:</h4>
<p style="color:red;">{returns.quantile(0.05):.2f}</p>
</div>
""", unsafe_allow_html=True)
# Function for time series forecast
def time_series_forecast(df):
st.subheader("Time Series Forecast")
logged_close = np.log(df['Close'])
st.line_chart(logged_close)
# Function for Covariance & Correlations analysis
def covariance_correlations(df):
st.subheader("Volatility Analysis")
returns = np.log(df['Close'] / df['Close'].shift(1)).dropna()
variance = returns.var()
std_dev = np.sqrt(variance)
st.markdown(f"""
<div style="background-color:#f0f2f6;padding:10px;border-radius:10px;margin-bottom:10px;">
<h4 style="color:#333;">Variance :</h4>
<p style="color:red;">{variance}</p>
</div>
<div style="background-color:#f0f2f6;padding:10px;border-radius:10px;">
<h4 style="color:#333;">Standard deviation:</h4>
<p style="color:red;">{std_dev}</p>
</div>
""", unsafe_allow_html=True)
# Function for Linear Regression analysis
def linear_regression(df):
st.subheader("Linear Regression (Graphical representation)")
df = df.dropna()
X = df[['Open']]
y = df['Close']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)
plt.scatter(X_train, y_train, color='blue')
plt.plot(X_train, model.predict(X_train), color='red')
plt.title("Linear Regression")
plt.xlabel("Open Price")
plt.ylabel("Close Price")
st.pyplot(plt)
plt.close()
# Function for Stock Statistics
def stock_statistics(df):
st.subheader("Stock Statistics")
returns = df['Close'].pct_change().dropna()
mean = returns.mean()
median = returns.median()
skewness = skew(returns)
kurt = kurtosis(returns)
jb_stat, pvalue = jarque_bera(returns)
col1, col2 = st.columns(2)
with col1:
st.markdown(f"""
<div style="background-color:#f0f2f6;padding:10px;border-radius:10px;">
<h4 style="color:#333;">Mean:</h4>
<p style="color:red;">{mean:.4f}</p>
<h4 style="color:#333;">Median:</h4>
<p style="color:red;">{median:.4f}</p>
</div>
""", unsafe_allow_html=True)
with col2:
st.markdown(f"""
<div style="background-color:#f0f2f6;padding:10px;border-radius:10px;">
<h4 style="color:#333;">Skew:</h4>
<p style="color:red;">{skewness:.4f}</p>
<h4 style="color:#333;">Kurtosis:</h4>
<p style="color:red;">{kurt:.4f}</p>
</div>
""", unsafe_allow_html=True)
st.markdown(f"""
<div style="background-color:#e8eaf6;padding:10px;border-radius:10px;">
<h4 style="color:#333;">Jarque-Bera Test:</h4>
<p style="color:red;">Statistic: {jb_stat:.2f}, P-value: {pvalue:.2e}</p>
</div>
""", unsafe_allow_html=True)
if pvalue > 0.05:
st.success("The returns are likely normal.")
else:
st.error("The returns are likely not normal.")
# Beta calculation (using NEAR-USD as market proxy)
def beta_calculation(df):
st.subheader("Market Sensitivity Analysis: NEAR-USD vs. BTC-USD")
# Download market data
market_df = yf.download('BTC-USD', df.index.min(), df.index.max())['Adj Close']
# Convert to timezone-naive datetime index if it's timezone-aware
if market_df.index.tz is not None:
market_df.index = market_df.index.tz_localize(None)
# Calculate returns and ensure timezone-naive index for NEAR data as well
market_ret = market_df.pct_change().dropna()
near_ret = df['Close'].pct_change().dropna()
if near_ret.index.tz is not None:
near_ret.index = near_ret.index.tz_localize(None)
# Align the data by date
aligned_data = pd.merge(market_ret.rename('Market'), near_ret.rename('NEAR'), left_index=True, right_index=True, how='inner')
# Perform linear regression
slope, intercept, r_value, p_value, std_err = linregress(aligned_data['Market'], aligned_data['NEAR'])
st.markdown(f"""
<div style="background-color:#f0f2f6;padding:10px;border-radius:10px;margin-bottom:10px;">
<h4 style="color:#333;">Beta (slope):</h4>
<p style="color:red;">{slope:.2f}</p>
</div>
<div style="background-color:#f0f2f6;padding:10px;border-radius:10px;">
<h4 style="color:#333;">Alpha (intercept):</h4>
<p style="color:red;">{intercept:.4f}</p>
</div>
""", unsafe_allow_html=True)
# Plotting the scatter plot of returns
plt.subplots()
plt.scatter(aligned_data['Market'], aligned_data['NEAR'], alpha=0.5)
plt.plot(aligned_data['Market'], intercept + slope * aligned_data['Market'], 'r', label='fitted line')
plt.xlabel('BTC-USD Returns')
plt.ylabel('NEAR-USD Returns')
plt.title('Market Sensitivity Analysis: NEAR-USD vs. BTC-USD')
plt.legend()
st.pyplot(plt)
plt.close()
def summarize_findings(df):
# Statistical Analysis Metrics
mean_return = df['Close'].pct_change().mean()
min_return = df['Close'].pct_change().min()
max_return = df['Close'].pct_change().max()
median_return = df['Close'].pct_change().median()
std_deviation = df['Close'].pct_change().std()
# Value at Risk Metric
inter_quantile_range = df['Close'].pct_change().quantile(0.05)
# Covariance & Correlations Analysis Metric
returns = np.log(df['Close'] / df['Close'].shift(1)).dropna()
variance = returns.var()
# Stock Statistics Metrics
skewness = skew(returns)
kurtosis_value = kurtosis(returns)
jb_stat, pvalue = jarque_bera(returns)
normality = "likely normal" if pvalue > 0.05 else "likely not normal"
# Get the model accuracy score from the stock_price_predictions function
model_accuracy_score = stock_price_predictions(df)
# Stock Price Predictions Metric
model_accuracy = model_accuracy_score # Now using the actual score from your analysis
# Fetch NEAR Blocks API data
near_blocks_data = fetch_near_blocks_stats()
high_24h = near_blocks_data.get("high_24h", "N/A")
high_all = near_blocks_data.get("high_all", "N/A")
low_24h = near_blocks_data.get("low_24h", "N/A")
low_all = near_blocks_data.get("low_all", "N/A")
change_24 = near_blocks_data.get("change_24", "N/A")
# Combine all findings into a summary
summary = f"""
- Mean Return: {mean_return:.4f}
- Min Return: {min_return:.4f}
- Max Return: {max_return:.4f}
- Median Return: {median_return:.4f}
- Standard Deviation: {std_deviation:.4f}
- Inter-Quantile Range: {inter_quantile_range:.4f}
- Variance: {variance:.4f}
- Skewness: {skewness:.4f}
- Kurtosis: {kurtosis_value:.4f}
- Jarque-Bera Test: {jb_stat:.2f}, P-value: {pvalue:.2e} ({normality})
- Model Accuracy Score: {model_accuracy}
- 24h High: {high_24h}
- All-Time High: {high_all}
- 24h Low: {low_24h}
- All-Time Low: {low_all}
- 24h Change: {change_24}
- Model Accuracy Score: {model_accuracy:.2f}
"""
return summary
def generate_prediction(summary, api_key):
prompt = format_stats_for_prompt(summary)
prediction = generate_ai_response(prompt, api_key)
return prediction
def summarize_anomalies(anomaly_dates):
summary = {}
summary = defaultdict(int)
for date in anomaly_dates:
month_year = date.strftime("%B %Y")
if month_year in summary:
summary[month_year] += 1
else:
summary[month_year] = 1
return summary
def generate_input_prompt(anomaly_summary):
prompt = "Here is a list of detected anomalies in particular months:\n\n"
for month_year, count in anomaly_summary.items():
prompt += f"{month_year}: {count} anomalies\n\n"
# Add more to the prompt as needed
return prompt
def anomaly_detection(df):
st.subheader("Anomaly Detection in NEAR-USD Trading Patterns")
data = df[['Close']].copy()
isolation_forest = IsolationForest(n_estimators=100, contamination='auto', random_state=42)
anomalies = isolation_forest.fit_predict(data)
data['Anomaly'] = anomalies
anomaly_data = data[data['Anomaly'] == -1]
plt.figure(figsize=(10, 6))
plt.plot(data.index, data['Close'], color='blue', label='Normal')
plt.scatter(anomaly_data.index, anomaly_data['Close'], color='red', label='Anomaly')
plt.title("Anomaly Detection in NEAR-USD Trading Patterns")
plt.xlabel("Date")
plt.ylabel("Close Price")
plt.legend()
st.pyplot(plt)
plt.close()
if not anomaly_data.empty:
anomaly_dates = sorted(anomaly_data.index.to_list()) # Ensure these are datetime objects
anomaly_summary = summarize_anomalies(anomaly_dates)
input_prompt = generate_input_prompt(anomaly_summary)
# Display the input prompt
st.markdown(f"<div style='padding: 10px; border-radius: 10px; background-color: #e1f5fe; margin-bottom: 10px;'>👤 <strong>Input prompt:</strong><br>{input_prompt}</div>", unsafe_allow_html=True)
analytics_prompt = generate_anomaly_analytics_prompt(anomaly_dates)
analytics_response = generate_ai_response_anomaly(analytics_prompt, st.secrets["API_KEY"])
# Splitting the response into individual lines and adding line breaks for Streamlit
response_lines = analytics_response.split("\n")
formatted_response = "<br>".join(response_lines)
st.markdown(f"""
<div style='padding: 10px; border-radius: 10px; background-color: #f0f4c3; margin-bottom: 10px;'>
🤖 <strong>Anomaly Analysis:</strong><br>{formatted_response}
</div>
""", unsafe_allow_html=True)
else:
st.markdown(f"""
<div style="background-color:#f0f2f6;padding:10px;border-radius:10px;margin-bottom:10px;">
<h4 style="color:#333;">Detected Anomalies:</h4>
<p style="color:red;">No anomalies detected with the current settings.</p>
</div>
""", unsafe_allow_html=True)
# Main app function
def app():
st.title('🕵🏻 Real Time Insights and Anomaly detection')
start_date = st.date_input("Start Date", value=pd.to_datetime('2023-01-01'))
end_date = st.date_input("End Date", value=pd.to_datetime('today'))
df = get_near_data(start_date, end_date)
if not df.empty:
display_basic_data(df)
statistical_analysis(df)
distribution_fitting(df)
value_at_risk(df)
time_series_forecast(df)
covariance_correlations(df)
stock_statistics(df)
beta_calculation(df)
linear_regression(df)
# Anomaly Detection
anomaly_detection(df)
# Generate summary and prediction
summary = summarize_findings(df)
prediction = generate_prediction(summary, st.secrets["API_KEY"])
st.subheader("Investment Outcome Prediction")
st.markdown(f"<div style='padding: 10px; border-radius: 10px; background-color: #f0f4c3; margin-bottom: 10px;'>🤖 <strong>Investment predictions response:</strong><br>{prediction}</div>", unsafe_allow_html=True)
if __name__ == "__main__":
app()