Using Deep Learning to Decode Candlestick Charts

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In today’s stock market, investors rely on three primary analysis methods: technical, fundamental, and sentiment (or positioning) analysis. Each approach has its own logic and dedicated followers, with strategies that can appear equally convincing in hindsight. However, no single method is universally superior—the choice often reflects an individual trader’s temperament, discipline, and risk tolerance.

For me, successful investing isn’t just about buying low and selling high (maximizing return). Just as critical is how quickly you achieve that return—what’s known as Internal Rate of Return (IRR). The longer a stock takes to appreciate, the more time erodes its effective yield. This focus on time sensitivity led me to explore one of the most time-dependent tools in trading: candlestick charts.

Candlestick patterns capture the psychological tug-of-war between bulls and bears through five key data points: open, close, high, low, and volume. Originating from 18th-century Japanese rice traders like Homma Munehisa, these patterns evolved into what’s now known as the "Sakata Method", influencing generations of technical analysts. But markets evolve—so do human emotions and price behaviors. Can traditional pattern recognition keep up?

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Why Traditional Pattern Recognition Falls Short

While classic candlestick formations—like bullish engulfing patterns, doji reversals, or island tops—offer valuable insights, they suffer from two major limitations:

  1. Subjectivity: Two traders may interpret the same chart differently.
  2. Static rules: A hammer pattern that worked in 2010 might fail in today’s algorithm-driven markets.

Markets are dynamic systems influenced by countless variables—news, macroeconomic shifts, algorithmic flows. What we need is a system that learns continuously and adapts: enter deep learning.

Introducing LSTM: The Memory-Based Market Analyst

Long Short-Term Memory (LSTM) networks are a type of Recurrent Neural Network (RNN) designed to recognize patterns in sequences of data—perfect for time series like stock prices.

Think of it this way:

“I grew up in France. I speak fluent ___.”

You didn’t need to see the word “French” to guess the answer. Your brain used contextual memory—linking “France” and “speak”—to infer the most likely outcome. That’s exactly what LSTM does.

Applied to finance, LSTM analyzes sequences of candlesticks to detect subtle signals:

Instead of relying on rigid rules, LSTM learns which combinations of price action tend to precede upward or downward moves—by training on historical data.

Core Keywords

Building an LSTM Model for Stock Forecasting

Let’s walk through a practical implementation using Foxconn (2317) daily data from 2013 to 2017. Our goal? Predict future closing prices based on past sequences.

Step 1: Data Preparation

We begin by loading the dataset into a Pandas DataFrame and removing any incomplete entries:

import pandas as pd
foxconndf = pd.read_csv('./foxconn_2013-2017.csv', index_col=0)
foxconndf.dropna(how='any', inplace=True)

To ensure stable model training, we normalize all values between 0 and 1 using Min-Max scaling:

from sklearn import preprocessing

def normalize(df):
    newdf = df.copy()
    min_max_scaler = preprocessing.MinMaxScaler()
    for col in ['open', 'low', 'high', 'volume', 'close']:
        newdf[col] = min_max_scaler.fit_transform(df[col].values.reshape(-1,1))
    return newdf

foxconndf_norm = normalize(foxconndf)

Step 2: Training and Testing Split

We define a 20-day lookback window—each input consists of 20 consecutive days of OHLCV data, used to predict the 21st day’s close:

import numpy as np

def data_helper(df, time_frame):
    number_features = len(df.columns)
    datavalue = df.values
    result = []

    for index in range(len(datavalue) - (time_frame + 1)):
        result.append(datavalue[index: index + (time_frame + 1)])

    result = np.array(result)
    train_size = round(0.9 * result.shape[0])

    x_train = result[:train_size, :-1]
    y_train = result[:train_size, -1, -1]  # Next day's close

    x_test = result[train_size:, :-1]
    y_test = result[train_size:, -1, -1]

    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], number_features))
    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], number_features))

    return [x_train, y_train, x_test, y_test]

X_train, y_train, X_test, y_test = data_helper(foxconndf_norm, 20)

Step 3: Constructing the LSTM Architecture

Using Keras, we build a deep LSTM model with dropout layers to prevent overfitting:

from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout

def build_model(input_length, input_dim):
    model = Sequential()
    model.add(LSTM(256, input_shape=(input_length, input_dim), return_sequences=True))
    model.add(Dropout(0.3))
    model.add(LSTM(256, return_sequences=False))
    model.add(Dropout(0.3))
    model.add(Dense(16, activation='relu'))
    model.add(Dense(1, activation='linear'))
    model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])
    return model

model = build_model(20, 5)

Step 4: Training and Prediction

Train over 50 epochs with a batch size of 128:

model.fit(X_train, y_train, batch_size=128, epochs=50, validation_split=0.1)

After training, generate predictions and reverse the normalization to compare real vs. predicted prices:

def denormalize(df, norm_value):
    original_values = df['close'].values.reshape(-1,1)
    scaler = preprocessing.MinMaxScaler()
    scaler.fit_transform(original_values)
    return scaler.inverse_transform(norm_value.reshape(-1,1))

pred = model.predict(X_test)
denorm_pred = denormalize(foxconndf, pred)
denorm_ytest = denormalize(foxconndf, y_test)

Evaluating Model Performance

When plotting actual versus predicted prices:

import matplotlib.pyplot as plt
plt.plot(denorm_pred, color='red', label='Prediction')
plt.plot(denorm_ytest, color='blue', label='Actual')
plt.legend()
plt.show()

The results show similar overall trends—but with a critical flaw: the prediction lags behind reality by several days.

This delay makes the model useless for real trading. A delayed signal means missed opportunities or late exits—essentially guaranteeing losses.

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Improving the Model

To reduce lag and improve accuracy, consider tuning:

Small tweaks can yield significant improvements in alignment between forecast and actuals.

Frequently Asked Questions (FAQ)

Q: Can LSTM accurately predict stock prices?
A: Not perfectly. Markets are influenced by unpredictable events. However, LSTM can identify probabilistic patterns in historical price behavior—useful for augmenting decision-making.

Q: Is deep learning better than traditional technical analysis?
A: It complements it. While humans excel at intuition and context, AI excels at processing vast datasets without bias or fatigue.

Q: Do I need coding skills to use AI in trading?
A: For building models like this one—yes. But many platforms now offer no-code AI tools for traders.

Q: Why did the prediction lag behind actual prices?
A: This often happens when models overfit to past trends instead of learning leading indicators. Regularization and better feature selection help reduce lag.

Q: Can this model work on crypto or forex markets?
A: Absolutely. Any time-series financial data—stocks, crypto, commodities—can be analyzed using similar architectures.

Q: How often should I retrain the model?
A: Ideally weekly or monthly to adapt to changing market regimes and avoid performance decay.

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