Algorithmic Trading & Indicators Advanced

Intraday Tick-Level Time & Sales Aggregator & Large Block Trade Detector in Python

Process raw tick trade logs in Python Pandas to aggregate volume bursts, aggressive buy/sell sweeps, and institutional block trade anomalies.

#pandas#numpy

Problem Statement

Institutional iceberg and block orders leave footprints in tick-level Time & Sales data. This script flags trades exceeding 5x average tick size.

Formula / Core Logic

Block Trade Flag = Trade_Volume > (Mean_Tick_Volume + 5 * Std_Tick_Volume)

Installation

pip install pandas numpy
intraday-time-and-sales-tape-reader-python.py
import pandas as pd
import numpy as np
\.def detect_block_trades(tick_df, threshold_mult=5.0):
    df = tick_df.copy()
    mean_vol = df['Size'].mean()
    std_vol = df['Size'].std()
    cutoff = mean_vol + (threshold_mult * std_vol)
    
    df['Aggressive_Buy'] = df['Price'] >= df['Ask']
    df['Block_Trade'] = df['Size'] >= cutoff
    return df[df['Block_Trade']]

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Frequently Asked Questions

Q: What is an institutional iceberg order?

A large parent order sliced into small visible chunks to hide execution intent; tape readers detect them via repeated volume clusters at identical price levels.