In this tutorial, we engage in building an advanced data analytics pipeline PolesA lightening-fast dataframe library is designed for optimal performance and scalability. Our goal is to demonstrate how we can use lazy evaluation of polers, complex manifestations, window functions and SQL interfaces to process large -scale financial datasets efficiently. We begin by generating a synthetic financial time chain dataset and move from feature engineering and rolling statistics to multi-dimensional analysis and rankings, carrying step-by-step via end-to-end pipeline. Throughout, we demonstrate how Dhruva gives us the right to write expressive and performing data changes while maintaining low memory use and ensuring rapid execution.
import polars as pl
import numpy as np
from datetime import datetime, timedelta
import io
try:
import polars as pl
except ImportError:
import subprocess
subprocess.run(["pip", "install", "polars"], check=True)
import polars as pl
print("🚀 Advanced Polars Analytics Pipeline")
print("=" * 50)We begin to import essential libraries, including poles for high-demonstration dataframe operations and numpy to generate synthetic data. To ensure compatibility, we add a followback installation step to the poles, if it is not already installed. With the setup being prepared, we indicate the introduction of our advanced analytics pipeline.
np.random.seed(42)
n_records = 100000
dates = [datetime(2020, 1, 1) + timedelta(days=i//100) for i in range(n_records)]
tickers = np.random.choice(['AAPL', 'GOOGL', 'MSFT', 'TSLA', 'AMZN'], n_records)
# Create complex synthetic dataset
data = {
'timestamp': dates,
'ticker': tickers,
'price': np.random.lognormal(4, 0.3, n_records),
'volume': np.random.exponential(1000000, n_records).astype(int),
'bid_ask_spread': np.random.exponential(0.01, n_records),
'market_cap': np.random.lognormal(25, 1, n_records),
'sector': np.random.choice(['Tech', 'Finance', 'Healthcare', 'Energy'], n_records)
}
print(f"📊 Generated {n_records:,} synthetic financial records")We generate a rich, synthetic financial dataset with 100,000 records using NUMPY, following daily stock data for major ticks such as AAPL and TSLA. Each entry includes major market features such as value, quantity, bid-back spread, market cap and sector. This time-series provides a realistic basis to demonstrate advanced Poles Analytics on the Time-series dataset.
lf = pl.LazyFrame(data)
result = (
lf
.with_columns([
pl.col('timestamp').dt.year().alias('year'),
pl.col('timestamp').dt.month().alias('month'),
pl.col('timestamp').dt.weekday().alias('weekday'),
pl.col('timestamp').dt.quarter().alias('quarter')
])
.with_columns([
pl.col('price').rolling_mean(20).over('ticker').alias('sma_20'),
pl.col('price').rolling_std(20).over('ticker').alias('volatility_20'),
pl.col('price').ewm_mean(span=12).over('ticker').alias('ema_12'),
pl.col('price').diff().alias('price_diff'),
(pl.col('volume') * pl.col('price')).alias('dollar_volume')
])
.with_columns([
pl.col('price_diff').clip(0, None).rolling_mean(14).over('ticker').alias('rsi_up'),
pl.col('price_diff').abs().rolling_mean(14).over('ticker').alias('rsi_down'),
(pl.col('price') - pl.col('sma_20')).alias('bb_position')
])
.with_columns([
(100 - (100 / (1 + pl.col('rsi_up') / pl.col('rsi_down')))).alias('rsi')
])
.filter(
(pl.col('price') > 10) &
(pl.col('volume') > 100000) &
(pl.col('sma_20').is_not_null())
)
.group_by(['ticker', 'year', 'quarter'])
.agg([
pl.col('price').mean().alias('avg_price'),
pl.col('price').std().alias('price_volatility'),
pl.col('price').min().alias('min_price'),
pl.col('price').max().alias('max_price'),
pl.col('price').quantile(0.5).alias('median_price'),
pl.col('volume').sum().alias('total_volume'),
pl.col('dollar_volume').sum().alias('total_dollar_volume'),
pl.col('rsi').filter(pl.col('rsi').is_not_null()).mean().alias('avg_rsi'),
pl.col('volatility_20').mean().alias('avg_volatility'),
pl.col('bb_position').std().alias('bollinger_deviation'),
pl.len().alias('trading_days'),
pl.col('sector').n_unique().alias('sectors_count'),
(pl.col('price') > pl.col('sma_20')).mean().alias('above_sma_ratio'),
((pl.col('price').max() - pl.col('price').min()) / pl.col('price').min())
.alias('price_range_pct')
])
.with_columns([
pl.col('total_dollar_volume').rank(method='ordinal', descending=True).alias('volume_rank'),
pl.col('price_volatility').rank(method='ordinal', descending=True).alias('volatility_rank')
])
.filter(pl.col('trading_days') >= 10)
.sort(['ticker', 'year', 'quarter'])
)
We load our synthetic datasate into a polar lazyframe that enabling the postponed performance, allows us to efficiently complex changes. From there, we enrich data with time-based features and implement advanced technical indicators, such as moving average, RSI, and Bollinger bands, windows and rolling functions. Then we perform grouped by ticker, year and quarter to remove major financial figures and indicators. Finally, we rank the results based on volume and instability, filter the under-traded segment, and sort data for intuitive investigation by taking advantage of the powerful lazy assessment engine of Polers for our full profit.
df = result.collect()
print(f"\n📈 Analysis Results: {df.height:,} aggregated records")
print("\nTop 10 High-Volume Quarters:")
print(df.sort('total_dollar_volume', descending=True).head(10).to_pandas())
print("\n🔍 Advanced Analytics:")
pivot_analysis = (
df.group_by('ticker')
.agg([
pl.col('avg_price').mean().alias('overall_avg_price'),
pl.col('price_volatility').mean().alias('overall_volatility'),
pl.col('total_dollar_volume').sum().alias('lifetime_volume'),
pl.col('above_sma_ratio').mean().alias('momentum_score'),
pl.col('price_range_pct').mean().alias('avg_range_pct')
])
.with_columns([
(pl.col('overall_avg_price') / pl.col('overall_volatility')).alias('risk_adj_score'),
(pl.col('momentum_score') * 0.4 +
pl.col('avg_range_pct') * 0.3 +
(pl.col('lifetime_volume') / pl.col('lifetime_volume').max()) * 0.3)
.alias('composite_score')
])
.sort('composite_score', descending=True)
)
print("\n🏆 Ticker Performance Ranking:")
print(pivot_analysis.to_pandas())Once our lazy pipeline is completed, we collect the results in dataframes and immediately review the top 10 quarters based on the total dollar. This helps us identify the duration of acute business activity. Then we take our analysis one step forward by grouping data by ticks to calculate high-level insights such as lifetime trading volumes, average value volatility and a custom composite score. This multi-dimensional summary helps us to compare stock not only by raw volume, but also by speed and risk-proper performance, unlocking deep insight into overall ticker behavior.
print("\n🔄 SQL Interface Demo:")
pl.Config.set_tbl_rows(5)
sql_result = pl.sql("""
SELECT
ticker,
AVG(avg_price) as mean_price,
STDDEV(price_volatility) as volatility_consistency,
SUM(total_dollar_volume) as total_volume,
COUNT(*) as quarters_tracked
FROM df
WHERE year >= 2021
GROUP BY ticker
ORDER BY total_volume DESC
""", eager=True)
print(sql_result)
print(f"\n⚡ Performance Metrics:")
print(f" • Lazy evaluation optimizations applied")
print(f" • {n_records:,} records processed efficiently")
print(f" • Memory-efficient columnar operations")
print(f" • Zero-copy operations where possible")
print(f"\n💾 Export Options:")
print(" • Parquet (high compression): df.write_parquet('data.parquet')")
print(" • Delta Lake: df.write_delta('delta_table')")
print(" • JSON streaming: df.write_ndjson('data.jsonl')")
print(" • Apache Arrow: df.to_arrow()")
print("\n✅ Advanced Polars pipeline completed successfully!")
print("🎯 Demonstrated: Lazy evaluation, complex expressions, window functions,")
print(" SQL interface, advanced aggregations, and high-performance analytics")We wrap the pipeline showing the elegant SQL interface of the Polers, running a total query with a subsequent ticker performance with SQL Syntax. This hybrid capacity enables us to clearly mix the expressive polar changes with SQL questions. To highlight its efficiency, we print out major performance matrix, emphasizing lazy evaluation, memory efficiency and zero-copy execution. Finally, we demonstrate how easily we can export results in different forms, such as parquet, arrow and jsonl, make this pipeline both powerful and production-taiyar. With this, we complete a full-circle, high-performance analytics workflow using Polers.
Finally, we have seen for the first time how the lazy API of Poles can adapt to complex analytics workflows that would otherwise be dull in traditional devices. We have developed a comprehensive financial analysis pipeline, which extends from raw data ingestion to rolling indicators, grouped aggregation and advanced scoring, all have been executed with blazing speed. Not only this, but we also tapped in the powerful SQL interface of Poles to run familiar questions on our dataframe. This dual ability to write both the expressions of the functional-style and SQL makes the polers an incredibly flexible tool for any data scientist.
Check it paper, All credit for this research goes to the researchers of this project. Also, feel free to follow us Twitter And don’t forget to join us 100k+ mL subredit More membership Our newspaper,
Sana Hasan, a counseling intern and double degree student at Marktekpost in IIT Madras, is emotional about implementing technology and AI to resolve real -world challenges. With a keen interest in solving practical problems, he brings a new approach to the intersection of AI and real -life solutions.
