Udemy - Statistical Inferencing For Quantitative Trading Strategi...

  • Category Other
  • Type Tutorials
  • Language English
  • Total size 2.4 GB
  • Uploaded By freecoursewb
  • Downloads 224
  • Last checked 14 hours ago
  • Date uploaded 5 months ago
  • Seeders 8
  • Leechers 7

Infohash : 45FFDCFC63366E92103E45B59D6DF44063ED5B9B



Statistical Inferencing For Quantitative Trading Strategies

https://WebToolTip.com

Last updated 7/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.41 GB | Duration: 4h 8m

Learn how to apply probability theory and statistical inferencing techniques to validate algorithmic trading strategies.

What you'll learn
Learn basics for finance and probability theory for algorithmic trading.
Learn statistical inferencing techniques such as parametric and nonparametric hypothesis tests.
Employ statistical learning techniques on quantitative trading strategies in Python.
Learn practical validation methods quants use before taking strategies into production.

Requirements
Basic-intermediate Python programming.

Files:

[ WebToolTip.com ] Udemy - Statistical Inferencing For Quantitative Trading Strategies
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here !
    • 1 - Introduction English.vtt (7.6 KB)
    • 1 - Introduction.mp4 (75.6 MB)
    • 10 - Monte Carlo Permutation Tests English.vtt (9.0 KB)
    • 10 - Monte Carlo Permutation Tests.mp4 (34.9 MB)
    • 11 - Univariate Null Distributions English.vtt (8.6 KB)
    • 11 - Univariate Null Distributions.mp4 (80.7 MB)
    • 11 - permutation-algorithms.pdf (639.7 KB)
    • 12 - Multivariate Null Distributions English.vtt (15.3 KB)
    • 12 - Multivariate Null Distributions.mp4 (104.0 MB)
    • 13 - Python Implementation for Statistical Null English.vtt (11.6 KB)
    • 13 - Python Implementation for Statistical Null.mp4 (105.1 MB)
    • 14 - Constructing the Hypothesis Test English.vtt (9.8 KB)
    • 14 - Constructing the Hypothesis Test.mp4 (69.8 MB)
    • 14 - nonparametric-permutation-test.pdf (285.1 KB)
    • 15 - Hypothesis Test for Model Overfitting on Trading Strategy English.vtt (8.0 KB)
    • 15 - Hypothesis Test for Model Overfitting on Trading Strategy.mp4 (128.3 MB)
    • 15 - lecture-notes.pdf (1.1 MB)
    • 16 - Hypothesis Test for Trading Strategy on OOS Data English.vtt (11.0 KB)
    • 16 - Hypothesis Test for Trading Strategy on OOS Data.mp4 (146.4 MB)
    • 17 - Python Implementation Analysis on Trend Following English.vtt (14.7 KB)
    • 17 - Python Implementation Analysis on Trend Following.mp4 (285.8 MB)
    • 18 - Hypothesis Test for Selection Bias across Multiple Strategies English.vtt (19.1 KB)
    • 18 - Hypothesis Test for Selection Bias across Multiple Strategies.mp4 (256.3 MB)
    • 19 - Python Implementation of the RomanoWolf Stepdown English.vtt (15.5 KB)
    • 19 - Python Implementation of the RomanoWolf Stepdown.mp4 (289.8 MB)
    • 2 - Finance Basics English.vtt (23.5 KB)
    • 2 - Finance Basics.mp4 (103.4 MB)
    • 2 - returns.pdf (290.0 KB)
    • 3 - Probability Statistics Basics English.vtt (8.9 KB)
    • 3 - Probability Statistics Basics.mp4 (100.5 MB)
    • 3 - random-variables.pdf (350.4 KB)
    • 4 - Inferences and Hypothesis Testing English.vtt (12.8 KB)
    • 4 - Inferences and Hypothesis Testing.mp4 (73.3 MB)
    • 4 - hypothesis-testing.pdf (436.3 KB)
    • 5 - Multiple Testing and Inferencing Errors English.vtt (10.5 KB)
    • 5 - Multiple Testing and Inferencing Errors.mp4 (71.1 MB)
    • 6 - Parametric Tests for Expected Returns English.vtt (12.8 KB)
    • 6 - Parametric Tests for Expected Returns.mp4 (133.3 MB)
    • 6 - parametric-tests.pdf (453.1 KB)
    • 7 - Nonparametric Tests for Median Returns English.vtt (12.1 KB)
    • 7 - Nonparametric Tests for Median Returns.mp4 (123.2 MB)
    • 7 - nonparametric-tests.pdf (334.4 KB)
    • 7 - regression profiling of trend strategies.txt (0.0 KB)
    • 8 - Python Implementation for Backtesting Strategies English.vtt (12.4 KB)
    • 8 - Python Implementation for Backtesting Strategies.mp4 (98.3 MB)
    • 9 - Python Implementation for Portfolio ReturnsMeans English.vtt (18.9 KB)
    • 9 - Python Implementation for Portfolio ReturnsMeans.mp4 (186.1 MB)
    • Bonus Resources.txt (0.1 KB)
    • README.md (1.0 KB)
    • STATISTICAL INFERENCING FOR QUANTITATIVE (0.1 KB)
    • __MACOSX
      • _README.md (0.2 KB)
      • _STATISTICAL INFERENCING FOR QUANTITATIVE (0.2 KB)
      • _disclaimer.txt (0.2 KB)
      • _location_tests.py (0.2 KB)
      • _main.py (0.2 KB)
      • _ohlcvs.pickle (0.2 KB)
      • _permutation_tests.py (0.2 KB)
      • _permutations.py (0.2 KB)
      • _quantpylib (0.2 KB)
      • _requirements.txt (0.2 KB)
      • _strategies.py (0.2 KB)
      • quantpylib
        • _simulator (0.2 KB)
        • _throttler (0.2 KB)
        • _wrappers (0.2 KB)
        • simulator
          • _.DS_Store (0.1 KB)
          • ___pycache__ (0.2 KB)
          • __pycache__
            • _alpha.cpython-311.pyc (0.2 KB)
          • _alpha.py (0.2 KB)
          • throttler
            • ___init__.py (0.2 KB)
            • ___pycache__ (0.2 KB)
            • __pycache__
              • ___init__.cpython-311.pyc (0.2 KB)
              • _aiosonic.cpython-311.pyc (0.2 KB)
              • _decorators.cpython-311.pyc (0.2 KB)
              • _exceptions.cpython-311.pyc (0.2 KB)
              • _rate_semaphore.cpython-311.pyc (0.2 KB)
            • _aiosonic.py (0.2 KB)
            • _decorators.py (0.2 KB)
            • _exceptions.py (0.2 KB)
            • _rate_semaphore.py (0.2 KB)
            • wrappers
              • ___init__.py (0.2 KB)
              • ___pycache__ (0.2 KB)
              • __pycache__
                • ___init__.cpython-311.pyc (0.2 KB)
                • ___init__.cpython-313.pyc (0.2 KB)
                • _binance.cpython-311.pyc (0.2 KB)
                • _binance.cpython-313.pyc (0.2 KB)
              • _binance.py (0.2 KB)
              • disclaimer.txt (0.9 KB)
              • location_tests.py (2.3 KB)
              • main.py (2.9 KB)
              • ohlcvs.pickle (371.3 KB)
              • permutation_tests.py (3.5 KB)
              • permutations.py (6.6 KB)
              • quantpylib simulator
                • DS_Store (6.0 KB)
                • __pycache__
                  • alpha.cpython-311.pyc (15.9 KB)
                • alpha.py (9.1 KB)
                • throttler
                  • __init__.py (0.0 KB)
                  • __pycache__
                    • __init__.cpython-311.pyc (0.2 KB)
                    • aiosonic.cpython-311.pyc (6.1 KB)
                    • decorators.cpython-311.pyc (8.4 KB)
                    • exceptions.cpython-311.pyc (1.5 KB)
                    • rate_semaphore.cpython-311.pyc (16.8 KB)
                  • aiosonic.py (4.7 KB)
                  • decorators.py (6.6 KB)
                  • exceptions.py (0.7 KB)
                  • rate_semaphore.py (12.7 KB)
                  • wrappers
                    • __init__.py (0.0 KB)
                    • __pycache__
                      • __init__.cpython-311.pyc (0.2 KB)
                      • __init__.cpython-313.pyc (0.2 KB)
                      • binance.cpython-311.pyc (9.7 KB)
                      • binance.cpython-313.pyc (8.7 KB)
                    • binance.py (6.3 KB)
                    • requirements.txt (0.1 KB)
                    • strategies.py (1.9 KB)

There are currently no comments. Feel free to leave one :)

Code:

  • udp://tracker.torrent.eu.org:451/announce
  • udp://tracker.tiny-vps.com:6969/announce
  • http://tracker.foreverpirates.co:80/announce
  • udp://tracker.cyberia.is:6969/announce
  • udp://exodus.desync.com:6969/announce
  • udp://explodie.org:6969/announce
  • udp://tracker.opentrackr.org:1337/announce
  • udp://9.rarbg.to:2780/announce
  • udp://tracker.internetwarriors.net:1337/announce
  • udp://ipv4.tracker.harry.lu:80/announce
  • udp://open.stealth.si:80/announce
  • udp://9.rarbg.to:2900/announce
  • udp://9.rarbg.me:2720/announce
  • udp://opentor.org:2710/announce
GDRIVE-CACHE 📁 GD (hit) | ID: 1G8yCXfPa1... 📄 torrent 🕐 16 Jan 2026, 12:06:45 am IST ⏰ 10 Feb 2026, 12:06:41 am IST ✅ Valid for 23d 8h 🔄 Refresh Cache