# Graphs in Finance: Predicting Market Trends with Machine Learning

Have you ever heard of the term machine learning? Exciting, right? And how about finance? Fascinating, isn't it? Now, put them together, and you have some genuinely jaw-dropping possibilities. Graphs in Finance using machine learning is the perfect example of this fantastic combination.

You see, financial data is very complicated. It's not just about numbers and graphs; it involves many factors that influence the market. But fear not, my friends! With the help of graphs and machine learning, it is possible to understand and analyze these intricate factors accurately.

## What are Graphs in Finance?

Before we delve deeper, let's discuss graphs in finance. So, what are graphs in finance? Simply put, these are representations of data in the financial world that show the relationships between different financial assets, payments, etc. Graphs used in finance are similar to the ones used in computer science, mathematics, and other fields.

Graphs help in understanding the relationships between different factors and how they influence each other. In finance, graphs can be used to study asset prices, market trends, volatility, and so on. By analyzing these graphs, traders, analysts, and investors can make informed decisions about what to buy, sell, or hold.

## Machine Learning in Finance

Machine learning, on the other hand, is a field of computer science that enables machines to learn from data without being explicitly programmed. Machine learning algorithms use statistical models to make predictions based on patterns observed in data. These predictions improve over time as more data is fed into the algorithm.

The use of machine learning in finance is not a new concept, and it has been around for some time. For instance, credit bureaus use machine learning algorithms to assess a person's creditworthiness, predict loan defaults, and fraud detection. Similarly, investment banks use machine learning to identify profitable trading opportunities, optimize portfolios, and risk management.

## Predicting Market Trends with Graphs and Machine Learning

Now, let's talk about the most exciting part: predicting market trends with graphs and machine learning. With the help of machine learning algorithms, it is possible to analyze vast amounts of financial data quickly and accurately. By combining these algorithms with graphs, one can identify patterns in market trends and make informed decisions about trading and investment.

For instance, financial companies can use machine learning algorithms to predict the stock prices of companies based on their financial statements, stock price history, and other factors. Graphs can be used to visualize the relationships between these factors and how they influence each other. By analyzing these relationships, traders and investors can decide whether to buy or sell a particular stock.

## Case Study: Predicting Future Stock Prices with LSTM Networks

Let's take an example to understand how machine learning and graphs can be used to predict market trends. In this case, we will use Long Short-Term Memory (LSTM) networks, a type of recurrent neural networks, to predict future stock prices.

### Dataset

We will use the historical stock price data of Apple Inc. (AAPL) from Yahoo Finance. The dataset includes the opening, closing, high, and low prices of AAPL from January 01, 2010, to December 31, 2019, along with the trading volume.

### Data Preprocessing

Before we feed the data to the LSTM network, we need to preprocess it. First, we will extract the closing price from the dataset and normalize it between 0 and 1. We will use the MinMaxScaler from the scikit-learn package to accomplish this.

```
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
```

Next, we will split the data into training and testing sets. We will use the first 70% of the data for training and the remaining 30% for testing.

```
training_data_len = math.ceil(len(scaled_data) * 0.7)
train_data = scaled_data[0:training_data_len, :]
x_train = []
y_train = []
for i in range(60, len(train_data)):
x_train.append(train_data[i-60:i, 0])
y_train.append(train_data[i, 0])
x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
```

We will use the last 60 days' data to predict the next day's closing price. That's why we have taken a 60-day window of data and created input features of 60 days.

### Model Building

We will use the Keras API to build the LSTM network. The network will have three LSTM layers with 50 neurons each, followed by two dense layers with 25 and 1 neuron(s), respectively.

```
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50))
model.add(Dense(25))
model.add(Dense(1))
```

The model will be trained for 100 epochs with a batch size of 64.

```
model.fit(x_train, y_train, epochs=100, batch_size=64)
```

### Model Testing

We will use the trained model to predict the next day's closing price of AAPL. To do that, we will use the last 60 days' data from the training set to predict the next day's closing price. We will use inverse_transform to convert the normalized values back to actual stock prices.

```
test_data = scaled_data[training_data_len - 60:, :]
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(60, len(test_data)):
x_test.append(test_data[i-60:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
```

### Visualization

Finally, we will visualize the predicted and actual closing prices of AAPL for the testing set.

```
train = data[:training_data_len]
valid = data[training_data_len:]
valid['Predictions'] = predictions
plt.figure(figsize=(16,8))
plt.title('Model')
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close Price USD ($)', fontsize=18)
plt.plot(train['Close'])
plt.plot(valid[['Close', 'Predictions']])
plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')
plt.show()
```

As you can see from the graph, the LSTM network has predicted the closing price of AAPL for the testing set quite accurately.

## Conclusion

In conclusion, it is evident that the combination of graphs and machine learning can provide valuable insights into the financial world. With their help, it is possible to understand and analyze vast amounts of financial data accurately. Whether it is predicting future stock prices, analyzing market trends, or making investment decisions, graphs and machine learning have a lot to offer in the field of finance.

So, if you are interested in graphs and machine learning or the financial world, it's time to dive in and explore the endless possibilities. Who knows what exciting things you might discover?

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