mtrader 0.1.0

A high-performance algorithmic trading and data inspection library.

📈 mtrader

mtrader is a professional-grade Python library for quantitative traders and data scientists.
It works like a Swiss Army Knife for data inspection, letting you debug complex financial datasets with a single command.

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🛠 Feature: inspect()

inspect() is a Universal Data Explorer.

Instead of multiple debugging calls like:

• print()
• type()
• .info()
• .describe()

You run:

mt.inspect(data)

and get a high-density statistical report.

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💎 Key Capabilities

Deep Statistics
For each column:

• Count, Max, Min
• Mean, Std
• Median, Mode
• NaN count

Intelligent Headers

Displays:

• Exact Python <class>
• Data shape
• Object structure

Auto-Flattening for Tensors

3D tensors like:

(Tickers, Time, Features)

are flattened automatically for global statistical analysis.

Smart Dictionary Pivot

Dictionaries with equal-length lists are automatically converted into column tables.

Perfect for:

• Exchange APIs
• JSON feeds
• Trading signals

Auto Loader

Inspect files directly:

• CSV
• Excel
• JSON strings

Example:

mt.inspect("historical_prices.csv")

Stress‑Proof Logic

Safe statistical handling prevents crashes when:

• Data types are mixed
• Columns are empty
• Strings appear with numbers

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📊 Example Output

Running:

mt.inspect(df)

Produces:

Data type: <class 'pandas.DataFrame'>
It is a pandas DataFrame. Total rows: 5. Total columns: 2. Summary:

column:      price      volume
 dtype:    float64       int64
     -      -----       -----
 Row 0:      100.5        1000
 Row 1:      101.2        1500
 Row 2:      100.8        1200
----------  -----       -----
 count:          5           5
   max:      102.1      2000.0
   min:      100.5      1000.0
  mean:     101.22      1500.0
   std:   0.622093  412.310563
median:      101.2      1500.0
  mode:      100.5      1000.0
   nan:          0           0

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🚀 Usage Examples

1. Pandas DataFrame

import mtrader as mt
import pandas as pd

df = pd.DataFrame({
    'price': [100.5, 101.2, 100.8, 102.1, 101.5],
    'volume': [1000, 1500, 1200, 1800, 2000]
})

mt.inspect(df)

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2. Pandas Series

series = pd.Series([10, 20, 30, 40, 50], name="RSI_Indicator")
mt.inspect(series)

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3. Smart Dictionary Pivot

data = {
    "Ticker": ["BTC", "ETH", "SOL"],
    "Price": [62000.5, 3400.2, 145.1],
    "Signal": ["Buy", "Hold", "Buy"]
}

mt.inspect(data)

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4. 3D NumPy Tensor

import numpy as np

# Shape: (2 Tickers, 100 Days, 5 Features)
tensor_data = np.random.randn(2, 100, 5)

mt.inspect(tensor_data)

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5. Auto‑Loading Files

mt.inspect("historical_prices.csv")

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6. JSON API Responses

json_response = '{"Symbol": ["AAPL", "TSLA"], "Price": [150.2, 700.5]}'

mt.inspect(json_response)

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⚙️ Parameters

Parameter Type Default Description

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df Any Required Data object, file path, or JSON string count int 3 Rows shown from top & bottom silent bool False Hide headers and show only table

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📦 Installation

Clone:

git clone https://github.com/yourusername/mtrader_lib.git

Install dependencies:

pip install pandas numpy openpyxl

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⭐ Why mtrader

Financial datasets are often:

• Large
• Messy
• Multi‑dimensional

mtrader provides instant visibility so you can:

• Debug faster
• Validate datasets
• Build trading models quicker

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📜 License

MIT License