In this tutorial, we immerse ourselves in the construction of an advanced data analysis pipeline using PolarA DataFrame Data-Ul-Rapid library designed for optimal performance and scalability. Our goal is to demonstrate how we can use the lazy polar evaluation, complex expressions, window functions and the SQL interface to effectively process large -scale financial data sets. We start by generating a set of data from synthetic financial chronological series and we move step by step via an end -to -end pipeline, from the engineering of functionalities and rolling statistics to a multidimensional analysis and classification. Throughout, we demonstrate how Polar allows us to write expressive and efficient data transformations, while maintaining low use of memory 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 start by importing essential libraries, including fleeces for high performance and NUMPY data data operations to generate synthetic data. To ensure compatibility, we add a rescue installation stage for fleeces in case it was not already installed. With the ready configuration, we point out the start of our advanced analysis 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 set of rich and synthetic financial data with 100,000 records using Numpy, simulating daily stock data for the main TICkers such as AAPPL and TSLA. Each entry includes key market features such as price, volume, bizarre distribution, market capitalization and the sector. This provides a realistic base to demonstrate advanced polar analyzes on a set of chronological series data.
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 data set in a Lazyframe thriller to allow delayed execution, allowing us to effectively chain complex transformations. From there, we enrich the data with time -based features and apply advanced technical indicators, such as mobile averages, RSI and Bollinger strips, using window and rolling functions. We then carry out aggregations grouped by Ticker, year and quarter to extract financial statistics and key indicators. Finally, we classify the results according to volume and volatility, filter under-negotiated segments and sort data for intuitive exploration, while taking advantage of the powerful polar evaluation engine to its advantage.
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 finished, we collect the results in a dataframe and immediately pass the 10 best quarters according to the total volume in dollars. This helps us to identify periods of intense commercial activity. We then go further from our analysis by grouping the data from Ticker to calculate higher level information, such as life trading volume, average price volatility and a personalized composite score. This multidimensional summary helps us to compare the actions not only by raw volume, but also by the momentum and the performance adjusted to the risk, unlocking deeper information on the overall behavior of the ticker.
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 finish the pipeline by presenting an elegant SQL interface of polar, performing a global request to analyze the performance of the post-2021 ticker with a familiar SQL syntax. This hybrid capacity allows us to mix the transformations of expressive polaries with declarative SQL queries transparently. To highlight its effectiveness, we print key performance measures, emphasizing the lazy evaluation, the effectiveness of memory and the execution of zero copying. Finally, we demonstrate how much we can export the results in various formats, such as Parquet, Arrow and JSONL, which makes this pipeline both powerful and ready for production. With this, we complete a high performance analysis workflow in a full circle using fleeces.
In conclusion, we have seen in the first hand how the lazy Polar API can optimize complex analysis workflows which would otherwise be slow in traditional tools. We have developed a complete financial analysis pipeline, from the ingestion of raw data to rolling indicators, to group aggregations and an advanced score, all executed at an outbreak. Not only that, but we also exploited a powerful SQL interface of Polar to execute familiar requests transparent on our data. This double capacity to write both functional and SQL style expressions makes polar an incredibly flexible tool for any scientist of the data.
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Sana Hassan, consulting trainee at Marktechpost and double -degree student at Iit Madras, is passionate about the application of technology and AI to meet the challenges of the real world. With a great interest in solving practical problems, it brings a new perspective to the intersection of AI and real life solutions.
