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00_odsc_lon_2023.md

AI-Powered Algorithmic Trading with Python

ODSC London 2023 Half-Day Training

Dr. Yves J. Hilpisch
CEO The Python Quants | The AI Machine
Adjunct Professor of Computational Finance

London, 15. June 2023

Short Link

http://bit.ly/odsc_ldn_2023

Slides

You find the slides under https://certificate.tpq.io/odsc_ldn_2023.pdf

Blog Post

Read the blog post that gives you a quick overview and introduction: https://opendatascience.com/ai-powered-algorithmic-trading-with-python/

Abstract

This half-day trading session covers several important Python topics and skills to apply AI and Machine Learning (ML) to Algorithmic Trading. The session shows how to make use of the Oanda trading API (via a demo account) to retrieve data, stream data, place orders, etc. Building on this, a ML-based trading strategy is formulated and backtested. Finally, the trading strategy is transformed into an online trading algorithm and is deployed for real-time trading on the Oanda trading platform.

Session Outline

  1. Module: Setting up the Python and Oanda (paper) trading infrastructure
  2. Module: Financial data logistics and backtesting of an ML-based algorithmic trading strategy
  3. Module: Deployment of the ML-based algorithmic trading strategy in real-time

Background knowledge needed

Basic knowledge of Python and data science packages, such as NumPy, pandas, and matplotlib.

Python Mastery in Finance Program

Certificate Program in Python for Finance: https://certificate.tpq.io

Python for Finance Basics Bootcamp: https://youtube.com/@dyjh

Financial Theory with Python

Our newest book about Financial Theory with Python (https://home.tpq.io/books/ftwp/).

Sign up under https://finpy.pqp.io to access all the Jupyter Notebooks and execute them on our Quant Platform.

Python for Algorithmic Trading

Our recent book about Python for Algorithmic Trading (https://py4at.tpq.io).

Sign up under https://py4at.pqp.io to access all the Jupyter Notebooks and execute them on our Quant Platform.

Artificial Intelligence in Finance

Our recent book about Artificial Intelligence in Finance (https://aiif.tpq.io).

Sign up under https://aiif.pqp.io to access all the Jupyter Notebooks and execute them on our Quant Platform.

Python for Finance (2nd ed.)

Our standard reference book about Python for Finance (http://py4fi.tpq.io).

Sign up under https://py4fi.pqp.io to access all the Jupyter Notebooks and execute them on our Quant Platform.

Further Resources

Python

If you have either Miniconda or Anaconda already installed, there is no need to install anything new.

Otherwise, you might want to install Miniconda for your operating system: https://conda.io/en/master/miniconda.html

Read more about the management of environments under: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html

Cloud

Use this link to get a 200 USD free credit for 60 days on DigitalOcean when signing up for a new account.

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02_odsc_lon_2023.ipynb
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"source": [
"# AI-Powered Algorithmic Trading with Python\n",
"\n",
"**ODSC London 2023**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dr. Yves J. Hilpisch | The Python Quants & The AI Machine\n",
"\n",
"https://tpq.io | https://aimachine.io | [@dyjh](http://twitter.com/dyjh)"
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"source": [
"### Case Study: AI-Powered Strategy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Oanda API "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import tpqoa\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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": 3,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.simplefilter('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"oanda = tpqoa.tpqoa('../oanda.cfg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Data "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 11435 entries, 2023-06-14 00:00:00 to 2023-06-14 21:57:55\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 o 11435 non-null float64\n",
" 1 h 11435 non-null float64\n",
" 2 l 11435 non-null float64\n",
" 3 c 11435 non-null float64\n",
" 4 volume 11435 non-null int64 \n",
" 5 complete 11435 non-null bool \n",
"dtypes: bool(1), float64(4), int64(1)\n",
"memory usage: 547.2 KB\n",
"CPU times: user 341 ms, sys: 15.4 ms, total: 356 ms\n",
"Wall time: 1.69 s\n"
]
}
],
"source": [
"%%time\n",
"data = oanda.get_history(\n",
" instrument='BCO_USD',\n",
" start='2023-06-14',\n",
" end='2023-06-15',\n",
" granularity='S5',\n",
" price='M'\n",
")\n",
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"data['r'] = np.log(data['c'] / data['c'].shift(1))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"data['d'] = np.sign(data['r'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Strategy "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"lags = 3"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"cols = list()\n",
"for lag in range(1, lags + 1):\n",
" col = f'lag_{lag}'\n",
" data[col] = data['r'].shift(lag)\n",
" cols.append(col)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>o</th>\n",
" <th>h</th>\n",
" <th>l</th>\n",
" <th>c</th>\n",
" <th>volume</th>\n",
" <th>complete</th>\n",
" <th>r</th>\n",
" <th>d</th>\n",
" <th>lag_1</th>\n",
" <th>lag_2</th>\n",
" <th>lag_3</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2023-06-14 00:00:00</th>\n",
" <td>74.159</td>\n",
" <td>74.164</td>\n",
" <td>74.159</td>\n",
" <td>74.164</td>\n",
" <td>2</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-06-14 00:00:05</th>\n",
" <td>74.169</td>\n",
" <td>74.169</td>\n",
" <td>74.139</td>\n",
" <td>74.139</td>\n",
" <td>8</td>\n",
" <td>True</td>\n",
" <td>-0.000337</td>\n",
" <td>-1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-06-14 00:00:10</th>\n",
" <td>74.159</td>\n",
" <td>74.159</td>\n",
" <td>74.134</td>\n",
" <td>74.134</td>\n",
" <td>3</td>\n",
" <td>True</td>\n",
" <td>-0.000067</td>\n",
" <td>-1.0</td>\n",
" <td>-0.000337</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-06-14 00:00:15</th>\n",
" <td>74.114</td>\n",
" <td>74.134</td>\n",
" <td>74.114</td>\n",
" <td>74.129</td>\n",
" <td>7</td>\n",
" <td>True</td>\n",
" <td>-0.000067</td>\n",
" <td>-1.0</td>\n",
" <td>-0.000067</td>\n",
" <td>-0.000337</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-06-14 00:00:25</th>\n",
" <td>74.124</td>\n",
" <td>74.124</td>\n",
" <td>74.114</td>\n",
" <td>74.114</td>\n",
" <td>2</td>\n",
" <td>True</td>\n",
" <td>-0.000202</td>\n",
" <td>-1.0</td>\n",
" <td>-0.000067</td>\n",
" <td>-0.000067</td>\n",
" <td>-0.000337</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" o h l c volume complete \\\n",
"time \n",
"2023-06-14 00:00:00 74.159 74.164 74.159 74.164 2 True \n",
"2023-06-14 00:00:05 74.169 74.169 74.139 74.139 8 True \n",
"2023-06-14 00:00:10 74.159 74.159 74.134 74.134 3 True \n",
"2023-06-14 00:00:15 74.114 74.134 74.114 74.129 7 True \n",
"2023-06-14 00:00:25 74.124 74.124 74.114 74.114 2 True \n",
"\n",
" r d lag_1 lag_2 lag_3 \n",
"time \n",
"2023-06-14 00:00:00 NaN NaN NaN NaN NaN \n",
"2023-06-14 00:00:05 -0.000337 -1.0 NaN NaN NaN \n",
"2023-06-14 00:00:10 -0.000067 -1.0 -0.000337 NaN NaN \n",
"2023-06-14 00:00:15 -0.000067 -1.0 -0.000067 -0.000337 NaN \n",
"2023-06-14 00:00:25 -0.000202 -1.0 -0.000067 -0.000067 -0.000337 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"data.dropna(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"data['d'] = data['d'].astype(int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train-Test Split"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9144"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"split = int(len(data) * 0.8)\n",
"split"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"train = data.iloc[:split].copy()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"mu, std = train.mean(), train.std()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"train_ = (train - mu) / std"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"test = data.iloc[split:].copy()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"test_ = (test - mu) / std"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training of the Model"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"model = MLPClassifier(hidden_layer_sizes=[24],\n",
" shuffle=False,\n",
" max_iter=500)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
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"text/plain": [
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"source": [
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing of the Model"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"test['p'] = model.predict(test_[cols])"
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},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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},
"execution_count": 25,
"metadata": {},
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],
"source": [
"accuracy_score(test['d'], test['p'])"
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},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"test['s'] = test['p'] * test['r']"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"r 0.992232\n",
"s 1.006379\n",
"dtype: float64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test[['r', 's']].sum().apply(np.exp)"
]
},
{
"cell_type": "code",
"execution_count": 28,
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