Identifying Stock Patterns Using Deep Learning

In the dynamic landscape of stock market trading, the fusion of technology and finance has birthed advanced methodologies for analyzing market trends and predicting future price movements.

This article delves into the intricate world of stock pattern identification using deep learning and Python’s extensive libraries. It’s tailored for traders, financial analysts, and algorithm developers who seek a deeper understanding of leveraging computational power for financial gains.

from collections import defaultdict

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
from statsmodels.nonparametric.kernel_regression import KernelReg
from yahoofinancials import YahooFinancials

Financial data analysis and algorithmic trading are handled with several Python libraries. The following are brief explanations of the roles of each imported library: 

collections.defaultdict: This library returns a default value when a key is missing. Using it, you could store and organize financial data effectively, where the keys reflect identifiable measures like dates or asset symbols and the values represent the corresponding variables. 

The numpy package provides support for large multi-dimensional arrays and matrices, along with high-level mathematical functions to work on these arrays. A trading algorithm can use NumPy to apply numerical analysis to price data and other financial indicators. It is especially suitable to work with tabular data like stock prices that can be imported as pandas as pd. In order to prepare data for trading decisions, Pandas can be used to manage time-series data, clean data, calculate statistics, and perform other tasks. 

A plotting library, matplotlib.pyplot, is used for making a wide variety of graphs and charts. Trading algorithms can use it to visualize financial information, such as stock price trends, indicators, or even the performance of trading strategies. 

It is a function in the SciPy library, which is used to do scientific computing and technical computing. In trading, the argrelextrema function helps identify local maxima and minima in price data, indicative of potential turning points or support and resistance levels in price data. 

statsmodels.nonparametric.kernel_regression.KernelReg: This submodule from Statsmodels provides non-parametric kernel regression functionality. Trading could use this method to fit a smooth curve to price data to identify trends without assuming the curve has a specific parametric form. 

YahooFinancials.YahooFinancials: This module gets financial data from Yahoo Finance. The platform allows you to access a huge amount of financial data, including stock prices, financial statements, and other market data, for analyzing and deciding what to do with your portfolio. 

The code is intended to set up a computer environment with all the tools necessary for developing algorithmic trading strategies. In this context, we mean retrievals, manipulations, statistical analysis, and visualizations of data. Despite the code block’s lack of any specific strategy, it provides the libraries that support the development of such strategies.

start_date = '2017-01-01'
end_date = '2017-12-31'
stock_code = 'FB' # e.g. AMZN, GOOG, FB, NVDA