00_pff_bootcamp.md

Python for Finance Online Bootcamp

3-Day Bootcamp about Python, NumPy & pandas for Finance

Dr. Yves J. Hilpisch

CEO The Python Quants | The AI Machine

Online, May 2023

Short Link

http://bit.ly/pffbc_may_2023

GotoWebinar

https://attendee.gotowebinar.com/register/1260449774563154009

Quant Platform

https://bootcamp.pqp.io (registration)
https://base.pqp.io (login)

Slides

https://certificate.tpq.io/pffbc.pdf

Discord Server

https://discord.gg/eanaY3vfKp

Miniconda Installer

https://conda.io/en/main/miniconda.html

Docker

To install Docker see https://docs.docker.com/install/.

To run a Ubuntu-based Docker container, execute on the shell (Linux/Mac) the following:

docker run -ti -p 9999:9999 -h pffbc -v $(pwd):/root/live ubuntu:latest /bin/bash

Make sure to adjust the folder to be mounted accordingly.

Once, JupyterLab is installed, run it with the following command:

jupyter lab --allow-root --port 9999 --ip 0.0.0.0

Cloud

Use this link to get a 200 USD bonus on DigitalOcean when signing up for a new account.

Further Resources

• https://tpq.io
• https://hilpisch.com
• https://books.tpq.io
• https://youtube.com/@dyjh
• https://twitter.com/dyjh

01_pff_bootcamp.ipynb

02_pff_bootcamp.ipynb

03_pff_bootcamp.ipynb

04_pff_bootcamp.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "id": "fa243fab-53f5-4695-a635-36d48494cf75",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31868c69-bcb8-4fe6-9695-b8eb5c842a7e",
   "metadata": {},
   "source": [
    "# Python for Finance\n",
    "\n",
    "**Online Bootcamp**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9defc78-897b-47d3-9b65-89eb13b0b64f",
   "metadata": {
    "tags": []
   },
   "source": [
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "http://tpq.io | [[email protected]](mailto:[email protected]) | [@dyjh](http://twitter.com/dyjh)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "814afe13-660c-4ad5-b496-ad8e8866d339",
   "metadata": {},
   "source": [
    "## Structured Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fde2a6bd-684d-473c-a8f6-b72855cf3c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1efbb990-08b3-4b6d-9b40-c254bdfb4a26",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.linspace(0, 15, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "987dc743-947b-4c86-9d60-d4ce28176993",
   "metadata": {},
   "outputs": [],
   "source": [
    "a  # regular ndarray object ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0acaeb0-3a39-43b3-ac09-3dc79205b97a",
   "metadata": {},
   "outputs": [],
   "source": [
    "a.dtype  # ... has a single dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84d7daec-a72e-4d44-ade6-6ee5520beb5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = (1, 2.5, 'A')\n",
    "t2 = (2, 3.5, 'B')\n",
    "t3 = (3, 4.5, 'C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a4544c4-cf09-428d-9d28-91ab649ac4ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array([t1, t2, t3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dc68833-6451-43b6-908a-eadd32f23b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sa = np.array([t1, t2, t3], dtype='i8, f8, O')"
   ]
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  {
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   "execution_count": null,
   "id": "d4737a92-453e-4cec-969a-ff599c4bf11d",
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   "source": [
    "sa"
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   "execution_count": null,
   "id": "a6042a05-8488-4caa-a72d-064999d8d901",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = np.dtype({'names': list('xyz'), 'formats': ['i8', 'f8', 'O']})\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6fb577b-ff50-4cd4-9e17-792c00edc85b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sa = np.array([t1, t2, t3], dtype=dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a9a1306-0558-41a1-bba0-4b8abf0793fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "sa"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d6e5b1c-40bb-4dbe-93ea-be6a0ae7ea93",
   "metadata": {},
   "outputs": [],
   "source": [
    "sa['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd242c31-c541-44a2-97c9-29d80697e615",
   "metadata": {},
   "outputs": [],
   "source": [
    "sa['x'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8fc729b-ee12-4fc4-9930-d320346cb612",
   "metadata": {},
   "outputs": [],
   "source": [
    "sa['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad5b7142-eb59-4d66-84f5-4a21830fd609",
   "metadata": {},
   "outputs": [],
   "source": [
    "sa['z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e433d2bf-496b-4fcb-af9d-f451e262a47c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sa[1]"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50358904-cdfc-4031-a00c-b0adefb074de",
   "metadata": {},
   "outputs": [],
   "source": [
    "sa[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a406da-f5fe-436d-9432-b1bd4383369e",
   "metadata": {},
   "source": [
    "## Financial Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14713f8b-4f68-40a7-82e7-7a0ae59837a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54a14c40-ebc3-4418-9549-8394146cf42d",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://certificate.tpq.io/findata.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "843f4759-2f6f-43e2-8b35-9d4107f16ad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = requests.get(url).text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d14d2c89-e972-4b7a-8f49-ea0030a848df",
   "metadata": {},
   "outputs": [],
   "source": [
    "text[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6636bee8-4964-4281-83b6-3164d22e2a7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data.csv', 'w') as f:\n",
    "    f.write(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4426cd4c-c0ea-4655-bc00-b7c2c1824437",
   "metadata": {},
   "source": [
    "## Importing the Data with `numpy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "330718f7-c221-4985-bb2a-f415b70d5495",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.genfromtxt('data.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a0f4f95-aedb-45f7-b338-d3ee2612bd3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.genfromtxt('data.csv', delimiter=',', dtype=None, encoding=None)\n",
    "# ... gives a regular array with a single dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff116918-9f46-44dc-ad1b-df749e9d1549",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.genfromtxt('data.csv', delimiter=',', dtype=None, encoding=None,\n",
    "             names=True)\n",
    "# ... gives a structured array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "936b15c6-0ecc-484c-9b51-235d9d9f7e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0747bfd-7260-4783-8e2e-79264a9316f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['AAPLO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d75f1714-14f5-41bb-b647-69720da8c077",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import plt\n",
    "plt.style.use('seaborn-v0_8')\n",
    "%config InlineBackend.figure_format = 'svg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "011df3d3-2a95-42fe-84b6-af4cdd6aa8f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(data['EUR']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0638e94f-1e2e-46dd-b434-105165e9b05b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d1b7986-d734-4588-abd9-fd26b25ee161",
   "metadata": {},
   "outputs": [],
   "source": [
    "cf = lambda x: datetime.datetime.strptime(x, '%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34bb1d60-c54c-423a-b749-5888ad80ea46",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.genfromtxt('data.csv', delimiter=',', dtype=None, encoding=None,\n",
    "             names=True, converters={0: cf})\n",
    "# ... gives a structured array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3110f1e-fa0c-409a-ae81-9f8905eff99f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68ea56e8-2682-4828-a708-c41da30ffd9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(data['Date'], data['XAU']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5c0d59c-710e-42e0-b4e4-5c4ea4976703",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['EUR'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23e4f911-6f20-4e45-bec7-23eaad67ee39",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['EUR'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe43b9d1-d650-4b94-9baa-de0a44a916ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = np.log(data['EUR'][1:] / data['EUR'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c01cd81f-47e9-43bb-ba69-227cd14b701d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(lr, bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f4612a-5421-42f9-8e69-dc9b8b29ab02",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"mailto:[email protected]\">[email protected]</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> "
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

05_pff_bootcamp.ipynb

06_pff_bootcamp.ipynb

tick_client.py

#
# Simple Tick Data Client
#
import zmq

context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://127.0.0.1:5555')
socket.setsockopt_string(zmq.SUBSCRIBE, 'MSFT')

while True:
    msg = socket.recv_string()
    print(msg)

tick_collector.py

#
# Simple Tick Data Collector
#
import zmq

context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://127.0.0.1:5555')
socket.setsockopt_string(zmq.SUBSCRIBE, '')

raw = list()
while True:
    msg = socket.recv_string()
    print(msg)
    symbol, price = msg.split()
    raw.append((symbol, float(price)))

tick_dataframe.py

#
# Simple Tick Data Collector
# (using pandas DataFrame objects)
#
import zmq
import datetime
import pandas as pd

context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://127.0.0.1:5555')
socket.setsockopt_string(zmq.SUBSCRIBE, '')

data = pd.DataFrame()
while True:
    msg = socket.recv_string()
    t = datetime.datetime.now()
    print(msg)
    symbol, price = msg.split()
    df = pd.DataFrame({'symbol': symbol, 'price': float(price)}, index=[t])
    data = pd.concat((data, df))

tick_server.py

#
# Simple Tick Data Server
#
import zmq
import time
import random

context = zmq.Context()
socket = context.socket(zmq.PUB)
socket.bind('tcp://127.0.0.1:5555')

AAPL = 100.
MSFT = 100.

while True:
    if random.random() > 0.45:
        AAPL += random.gauss(0, 1) / 2
        msg = f'AAPL {AAPL:.3f}'
    else:
        MSFT += random.gauss(0, 1) / 2
        msg = f'MSFT {MSFT:.3f}'
    socket.send_string(msg)
    print(msg)
    time.sleep(random.random() * 2)