Have you ever found yourself staring at a NEPSE price chart, feeling like you’re missing half the story, as if crucial market signals are just whispers you can’t quite catch? I certainly have. Traditional financial models, while mathematically sound, often operate in a vacuum, overlooking the subtle ripples of external events that can significantly sway market sentiment and drive unexpected price movements. For a nascent market like NEPSE, where high-quality, real-time financial news APIs are either non-existent or prohibitively expensive, this data scarcity becomes a formidable engineering challenge. That’s precisely the conundrum I faced when trying to build a more perceptive forecasting system, leading me to experiment with unconventional, yet readily available, data sources like public RSS feeds. This post is for fellow engineers and data scientists looking to break free from purely numerical time series and integrate unstructured, dynamic text feeds – even seemingly unrelated ones like tech blogs – to build a more robust, multi-modal forecasting pipeline for NEPSE. I’ll share how I architected a system to pull, process, and align dynamic RSS content, extract actionable features, and integrate them to enrich our predictions, focusing on the practical engineering challenges of feature generation, temporal synchronization, and model deployment. My core judgment here is that even noisy, proxy textual signals, when properly engineered and aligned, can offer a tangible edge in forecasting volatile markets, providing a richer context that purely numerical data misses.
Key Takeaways
- Multi-modal feature engineering combines structured numerical data with unstructured text for improved time series forecasting, especially in data-scarce environments.
- A practical pipeline involves ingesting and parsing dynamic RSS feeds using
feedparser, then extracting actionable features (like TF-IDF scores) usingscikit-learn. - Effective strategies are crucial for temporally aligning disparate data streams (e.g., daily stock prices and irregularly published news events) for robust model training.
- Building and evaluating an
XGBoostregressor that leverages both traditional financial metrics and text-derived features can yield more comprehensive predictions. - Production deployment requires critical considerations for data freshness, robust error handling, and a clear understanding of the inherent tradeoffs of using proxy sentiment or topic signals.
The Problem: Beyond Numerical Limits in NEPSE Forecasting
Our previous work on anomaly detection and adaptive agents for NEPSE data often focused on the inherent patterns within the price series itself. While effective for identifying deviations from established norms, these models struggle to anticipate shifts driven by external factors. Imagine a sudden policy change, a new technological breakthrough, or even a widespread sentiment shift in a related industry – these events, often first articulated in text, precede their impact on stock prices. For the NEPSE market, direct, high-frequency news feeds are not always available, leaving a significant blind spot. My challenge was to find a way to capture these exogenous signals without access to premium financial news, turning to publicly available RSS feeds as a proxy for broader economic or technological sentiment that could indirectly influence market behavior. This isn't about direct financial news, but about the ripple effect of general economic or tech sentiment, which can often be gleaned from seemingly unrelated industry blogs.
Data and Sources
To demonstrate this multi-modal approach, we'll use two distinct data sources:
- Discord Engineering Blog RSS Feed: A real, dynamic RSS feed providing a stream of technical articles. We'll extract titles and summaries to represent unstructured text data, serving as a proxy for "market sentiment" or "innovation signals" that might subtly influence broader economic perception.
- RSS Feed URL: https://discord.com/blog/rss.xml
- Simulated NEPSE Historical Data: Since direct, comprehensive historical NEPSE data with an open API is not readily available for programmatic access in a blog post context, I've created a synthetic dataset that mimics the characteristics of typical stock market data (trend, seasonality, volatility). This allows us to focus on the integration challenge without relying on purely random numbers. The simulation ensures reproducibility by seeding random components. This is for demonstration purposes only and should not be used for actual financial decisions.
Data accessed on 2026-09-24.
Ingesting Dynamic RSS Feeds
The first hurdle in our multi-modal pipeline is reliably ingesting the unstructured text data. RSS feeds, while standard, can be tricky due to varying structures and potential parsing errors. I chose feedparser for its robustness in handling diverse RSS/Atom formats. The goal here is to fetch recent entries and extract their titles and summaries, which will form the basis of our textual features.
Fetching and Parsing RSS Entries
This snippet shows how I fetch the RSS feed, parse it, and extract the relevant text fields. I'm also capturing the publication date, which is crucial for later temporal alignment.
import feedparser
import pandas as pd
from datetime import datetime
def fetch_rss_data(rss_url):
"""Fetches and parses RSS feed, extracting relevant text and dates."""
try:
feed = feedparser.parse(rss_url)
if feed.bozo: # Check for well-formedness issues
print(f"Warning: RSS feed parsing issues for {rss_url}: {feed.bozo_exception}")
entries_data = []
for entry in feed.entries:
pub_date = None
if hasattr(entry, 'published_parsed') and entry.published_parsed:
pub_date = datetime(*entry.published_parsed[:6])
# Prioritize summary_detail.value, then summary, then description
summary_text = getattr(entry, 'summary_detail', {}).get('value') or \
getattr(entry, 'summary', '') or \
getattr(entry, 'description', '')
entries_data.append({
'title': getattr(entry, 'title', ''),
'summary': summary_text,
'published': pub_date,
'link': getattr(entry, 'link', '')
})
return pd.DataFrame(entries_data)
except Exception as e:
print(f"Error fetching or parsing RSS feed {rss_url}: {e}")
return pd.DataFrame() # Return empty DataFrame on error
I added error handling for malformed feeds (`feed.bozo`) and general exceptions during the network request or parsing. It's also important to standardize the extraction of the summary, as different feeds might use `summary`, `description`, or `summary_detail` fields. The `published_parsed` attribute from `feedparser` is a convenient way to get a UTC time tuple, which I convert to a `datetime` object for easier manipulation.
Extracting Meaningful Features from Text
Raw text isn't directly usable by most machine learning models. We need to convert it into numerical features. For this, I'm employing a simple yet effective technique: TF-IDF (Term Frequency-Inverse Document Frequency). TF-IDF highlights words that are important in a specific document but not overly common across all documents, helping us capture unique themes or "topics" from the RSS entries.
Vectorizing Text with TF-IDF
Here, I'm combining the title and summary of each RSS entry into a single text document. Then, I use TfidfVectorizer from scikit-learn to transform these texts into a matrix of TF-IDF features. I'm limiting the number of features to the top 1000 to keep the model manageable and avoid overfitting on rare terms, a common production concern.
from sklearn.feature_extraction.text import TfidfVectorizer
def vectorize_text_data(df):
"""Vectorizes text data using TF-IDF."""
if df.empty:
return None, None
# Combine title and summary for richer context
df['full_text'] = df['title'].fillna('') + ' ' + df['summary'].fillna('')
df['full_text'] = df['full_text'].str.strip()
vectorizer = TfidfVectorizer(max_features=1000, stop_words='english', ngram_range=(1,2))
tfidf_matrix = vectorizer.fit_transform(df['full_text'])
# Create DataFrame for easier merging later, using feature names as columns
tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
tfidf_df['published'] = df['published'].dt.date # Keep date for alignment
return tfidf_df, vectorizer
I've included `ngram_range=(1,2)` to capture common two-word phrases, which often carry more semantic meaning than single words. The `stop_words='english'` parameter removes common words that don't add much value. The output is a DataFrame where each row is an RSS entry and columns represent TF-IDF scores for different terms. I'm also explicitly storing the publication date (as a `date` object, not `datetime`) for the next crucial step.
Simulating NEPSE Price Data for Demonstration
To complete our multi-modal picture, we need numerical financial data. As mentioned, I'm generating a synthetic NEPSE-like time series. This simulation aims to capture key characteristics like an upward trend, some seasonality, and daily volatility, making it more realistic than purely random data. It's crucial for demonstrating the integration process, even if the data itself isn't real-world NEPSE prices.
Generating Plausible Stock Prices
import numpy as np
def generate_nepse_data(start_date, end_date, seed=42):
"""Generates synthetic NEPSE-like stock data."""
np.random.seed(seed) # Ensure reproducibility for noise
dates = pd.date_range(start=start_date, end=end_date, freq='B') # Business days
n_days = len(dates)
# Base trend
base_price = 1500
trend = np.linspace(0, 500, n_days) # Upward trend
# Seasonal component (e.g., yearly cycle)
seasonality = 50 * np.sin(np.linspace(0, 2 * np.pi * 2, n_days)) # Two cycles over the period
# Daily noise and volatility
daily_returns = np.random.normal(0.0005, 0.01, n_days).cumsum() # Small drift, daily volatility
# Combine components
prices = base_price + trend + seasonality + daily_returns * 100 # Scale returns to price changes
# Ensure prices are positive and somewhat realistic
prices = np.maximum(prices, 1000) # Floor price
df = pd.DataFrame({
'date': dates,
'open': prices,
'high': prices * (1 + np.random.uniform(0.005, 0.01, n_days)),
'low': prices * (1 - np.random.uniform(0.005, 0.01, n_days)),
'close': prices * (1 + np.random.uniform(-0.002, 0.002, n_days)), #
