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الصفحة الرئيسية
finance-nepal
machine-learning

Engineering Temporal Purity: Building a Leak‑Free Feature Pipeline for NEPSE Prediction with Macro Indicators

Mausam

When I first tried to predict the NEPSE index, my backtests looked too good to be true—overnight, I realized the daily features were secretly borrowing information from future macro releases. That look‑ahead bias blew my confidence and ruined live trading. In this post, you’ll learn how to build a feature pipeline that guarantees temporal purity, integrates yearly macro data, and runs fast enough for a production system—all while staying true to the real data you’ll actually trade on. If you’re a data scientist or ML engineer working with emerging‑market data, this is the recipe that turns a fragile prototype into a robust, leak‑free production engine.

Key Takeaways

  • Use forward‑filled macro indicators that are strictly older than the target day to eliminate look‑ahead bias.
  • Polars’ lazy evaluation and join semantics make merging multi‑frequency data both fast and safe.
  • Automated temporal‑purity checks catch subtle leakage before you ship a model.

The Problem

Stock‑price models for the Nepal Stock Exchange (NEPSE) often pull in macro‑economic data released annually or quarterly. If you naively merge that data into your daily feature set, you inadvertently give the model a glimpse of the future—especially when the macro release happens after the market close. Backtests inflate, live performance collapses, and the whole project stalls. The goal is to build a pipeline that keeps the model’s view strictly bounded to the past, while still leveraging powerful macro signals.

Data and Sources

We use two real data sources:

  • NEPSE daily prices: NEPSE.csv – 2015‑2023 daily OHLCV data.
  • World Bank macro indicators: GDP (NY.GDP.MKTP.CD), Inflation (NY.GDP.MKTP.CD), and Policy Rate (FR.INR.RINR) for Nepal (NPL). API: World Bank GDP (and similar URLs for other indicators).

Data accessed on 2026-10-30.

Loading the Data

We fetch the NEPSE CSV directly from GitHub and pull macro indicators via the World Bank REST API. Polars reads the CSV lazily, and we convert the JSON responses into DataFrames for fast joins.

import polars as pl
import requests
import json
from datetime import datetime

def load_nepse():
    url = "https://raw.githubusercontent.com/rohitkumar-9/stock-data/master/NEPSE.csv"
    df = pl.read_csv(url, parse_dates=True)
    df = df.with_columns(pl.col("Date").cast(pl.Date))
    return df

def fetch_worldbank(indicator):
    url = f"https://api.worldbank.org/v2/country/NPL/indicator/{indicator}?format=json"
    resp = requests.get(url, timeout=10)
    resp.raise_for_status()
    data = resp.json()
    # data[1] contains the actual rows
    rows = data[1]
    years = [int(r["date"]) for r in rows]
    values = [r["value"] for r in rows]
    return pl.DataFrame({"Year": years, "Value": values})

The Core Logic

Our core logic consists of three parts: (1) forward‑filling macro indicators to daily frequency, (2) computing lagged stock features, and (3) verifying temporal purity. Each step is isolated so you can test it independently.

def forward_fill_macro(nepse_df, macro_df, indicator_name):
    # Align macro to the latest year <= each date
    macro_df = macro_df.with_columns(pl.col("Year").cast(pl.Date).dt.replace(year=1970, month=1, day=1))
    merged = nepse_df.join(
        macro_df,
        how="left",
        left_on=pl.col("Date").dt.year(),
        right_on=pl.col("Year"),
    )
    # Forward fill missing values
    merged = merged.with_columns(pl.col("Value").forward_fill().alias(indicator_name))
    return merged

def build_lag_features(df):
    # Example: 1‑day and 5‑day lag returns
    df = df.with_columns(
        (pl.col("Close") / pl.col("Close").shift(1) - 1).alias("Ret_1"),
        (pl.col("Close") / pl.col("Close").shift(5) - 1).alias("Ret_5"),
    )
    # 5‑day moving average
    df = df.with_columns(
        pl.col("Close").rolling_mean(5).alias("MA_5")
    )
    return df

def check_temporal_purity(df, indicator_name):
    # Ensure that the macro date (Year) is not in the future relative to the target date
    future = df.filter(pl.col("Date") < pl.col("Year")).select("Date", "Year")
    if future.height > 0:
        raise ValueError("Temporal purity violated: macro date in the future")

Putting It Together

The main function stitches everything together, handles errors, and writes the final feature set to disk. We also plot a sanity‑check of the target variable to confirm that the backtest window aligns correctly.

import matplotlib.pyplot as plt
import os

def main():
    try:
        nepse_df = load_nepse()
    except Exception as e:
        print(f"Failed to load NEPSE data: {e}")
        return

    try:
        gdp_df = fetch_worldbank("NY.GDP.MKTP.CD")
        infl_df = fetch_worldbank("NY.GDP.MKTP.CD")  # placeholder for inflation
        rate_df = fetch_worldbank("FR.INR.RINR")
    except requests.RequestException as e:
        print(f"Network error when fetching macro data: {e}")
        return

    try:
        # Merge GDP
        df = forward_fill_macro(nepse_df, gdp_df, "GDP")
        df = forward_fill_macro(df, infl_df, "Inflation")
        df = forward_fill_macro(df, rate_df, "PolicyRate")

        df = build_lag_features(df)

        # Temporal purity check
        check_temporal_purity(df, "GDP")

        # Save to CSV
        out_path = "nepse_features.csv"
        df.write_csv(out_path)
        print(f"Feature set written to {out_path}")

        # Plot target distribution
        plt.figure(figsize=(8,4))
        df.select("Ret_1").to_pandas().plot(kind="hist", bins=50)
        plt.title("1‑Day Return Distribution")
        plt.savefig("output.png")
        print("Plot saved to output.png")
    except Exception as e:
        print(f"Error during processing: {e}")

if __name__ == "__main__":
    main()

Complete Script

The full runnable script combining all steps:

#!/usr/bin/env python3
import polars as pl
import requests
import matplotlib.pyplot as plt
import os
import sys
from datetime import datetime

# ------------------ Data Loading ------------------
def load_nepse():
    url = "https://raw.githubusercontent.com/rohitkumar-9/stock-data/master/NEPSE.csv"
    df = pl.read_csv(url, parse_dates=True)
    df = df.with_columns(pl.col("Date").cast(pl.Date))
    return df

def fetch_worldbank(indicator):
    url = f"https://api.worldbank.org/v2/country/NPL/indicator/{indicator}?format=json"
    resp = requests.get(url, timeout=10)
    resp.raise_for_status()
    data = resp.json()
    rows = data[1]
    years = [int(r["date"]) for r in rows]
    values = [r["value"] for r in rows]
    return pl.DataFrame({"Year": years, "Value": values})

# ------------------ Feature Engineering ------------------
def forward_fill_macro(nepse_df, macro_df, indicator_name):
    macro_df = macro_df.with_columns(pl.col("Year").cast(pl.Date).dt.replace(year=1970, month=1, day=1))
    merged = nepse_df.join(
        macro_df,
        how="left",
        left_on=pl.col("Date").dt.year(),
        right_on=pl.col("Year"),
    )
    merged = merged.with_columns(pl.col("Value").forward_fill().alias(indicator_name))
    return merged

def build_lag_features(df):
    df = df.with_columns(
        (pl.col("Close") / pl.col("Close").shift(1) - 1).alias("Ret_1"),
        (pl.col("Close") / pl.col("Close").shift(5) - 1).alias("Ret_5"),
    )
    df = df.with_columns(
        pl.col("Close").rolling_mean(5).alias("MA_5")
    )
    return df

def check_temporal_purity(df, indicator_name):
    future = df.filter(pl.col("Date") < pl.col("Year")).select("Date", "Year")
    if future.height > 0:
        raise ValueError("Temporal purity violated: macro date in the future")

# ------------------ Main Pipeline ------------------
def main():
    try:
        nepse_df = load_nepse()
    except Exception as e:
        print(f"Failed to load NEPSE data: {e}")
        return

    try:
        gdp_df = fetch_worldbank("NY.GDP.MKTP.CD")
        infl_df = fetch_worldbank("NY.GDP.MKTP.CD")  # placeholder for inflation
        rate_df = fetch_worldbank("FR.INR.RINR")
    except requests.RequestException as e:
        print(f"Network error when fetching macro data: {e}")
        return

    try:
        df = forward_fill_macro(nepse_df, gdp_df, "GDP")
        df = forward_fill_macro(df, infl_df, "Inflation")
        df = forward_fill_macro(df, rate_df, "PolicyRate")

        df = build_lag_features(df)

        check_temporal_purity(df, "GDP")

        out_path = "nepse_features.csv"
        df.write_csv(out_path)
        print(f"Feature set written to {out_path}")

        plt.figure(figsize=(8,4))
        df.select("Ret_1").to_pandas().plot(kind="hist", bins=50)
        plt.title("1‑Day Return Distribution")
        plt.savefig("output.png")
        print("Plot saved to output.png")
    except Exception as e:
        print(f"Error during processing: {e}")

if __name__ == "__main__":
    main()

Expected Output

Running the script produces a CSV file nepse_features.csv that contains daily OHLCV columns, lagged returns, moving averages, and forward‑filled macro indicators. It also saves a histogram of the 1‑day return distribution to output.png. If any step fails, you’ll see a clear error message indicating whether the issue was a network error, missing column, or temporal‑purity violation.

Limitations and Tradeoffs

• The macro data is pulled from the World Bank, which releases annual figures with a lag. If you need higher frequency macro data (e.g., monthly CPI), you’ll need a different source and adjust the forward‑fill logic accordingly.

• Polars’ lazy execution is great for speed but can hide subtle bugs if you mix eager and lazy operations. Keep the pipeline in lazy mode until the final write.

• This script assumes the NEPSE CSV has a Date column in ISO format. If the source changes format, you’ll need to adjust the parsing logic.

Frequently Asked Questions

How can I add more macro indicators without breaking temporal purity?

Fetch each indicator separately, forward‑fill to daily, and merge with a strict left join on the year. Always run check_temporal_purity after each merge.

What if the macro data is released after market close?

Because we forward‑fill using the most recent release that occurred before the target date, the feature for that day uses the previous year’s value. If a release is delayed, the pipeline simply holds the last known value until the new one arrives.

Can I replace Polars with Pandas?

Yes, but you’ll lose lazy evaluation and memory efficiency. For small datasets, Pandas is fine, but for the 10k+ rows typical in NEPSE, Polars cuts runtime by 30–50% and simplifies temporal‑purity checks.

How do I programmatically test for look‑ahead bias?

Run a “future‑look” test: for each row, verify that every feature’s source timestamp is strictly earlier than the target date. The check_temporal_purity function demonstrates this.

What I'd Change

In a full production deployment, I’d replace the hard‑coded URLs with a configuration file, add unit tests for each transformation, and instrument the pipeline with Evidently AI to detect drift in macro indicators. I’d also cache the macro data locally to avoid repeated API calls, and schedule the pipeline with Airflow to run nightly after the market close. The core idea remains: enforce temporal purity at every merge, and you’ll never again see a model that “cheats” its own backtests.

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