Optimizing Financial Strategies: Harnessing Machine Learning for Enhanced Trading Performance

Leveraging Alpaca API and Advanced Analytics to Navigate Market Volatility and Maximize Returns

In this exploration of cutting-edge trading technology, we introduce a sophisticated trading bot, engineered to leverage a decade of financial data via the Alpaca API. The bot’s foundation is rooted in machine learning, utilizing the alpaca.getbars() function for data access and focusing on a moving average crossover strategy. This strategy, pivotal to its operation, hinges on the interaction between the 2-day and 200-day Simple Moving Averages (SMAs), a technique aimed at capturing market trends and volatilities.

The setup involves critical libraries like Pandas for data processing, Matplotlib for visualization, and SKLearn for machine learning model implementation. The article outlines the configuration of Alpaca API keys, data retrieval, preprocessing, and the application of machine learning models, including Support Vector Machines (SVM) and Logistic Regression. It delves into model training, testing on historical data, and evaluation using classification reports and return analyses, emphasizing the significance of feature scaling and selection. The culmination of this technical journey is the analysis of trade signals and the financial efficacy of the strategy, measured by profit/loss and ROI metrics, presenting a nuanced blend of algorithmic trading and machine learning.

Download the source code from the link in comment section

This bot is a sophisticated algorithm that utilizes 10 years of financial data obtained from the Alpaca API. It employs the alpaca.getbars() function, which can access up to 1000 trading days of data.

For training, the bot uses a one-year period. This period is divided such that 75% covers the time leading up to the pandemic-induced market crash, and the remaining 25% includes the crash period and the initial phase of the market recovery.

The trading strategy of the bot is based on moving average crossovers. It executes trades when the 2-day Simple Moving Average (SMA) intersects with the 200-day SMA.

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# Import the required libraries and dependencies
import os
import requests
import pandas as pd
from dotenv import load_dotenv
import alpaca_trade_api as tradeapi
%matplotlib inline
from alpaca_trade_api.rest import TimeFrame

import numpy as np
from pathlib import Path
import hvplot.pandas
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.preprocessing import StandardScaler
from pandas.tseries.offsets import DateOffset
from sklearn.metrics import classification_report