As a data scientist in Nepal, I've often struggled with the limitations of traditional machine learning approaches in capturing the complexity and volatility of financial markets. The need for real-time insights and faster decision-making has become increasingly crucial, prompting me to explore the potential of edge AI in revolutionizing data science for Nepal's financial sector. In this post, we'll delve into the process of building a highly accurate and efficient forecasting model using real-time time series data from the Nepal Stock Exchange (NEPSE) API, and discuss the benefits and challenges of implementing edge AI in production.
Key Takeaways
- Edge AI can be used to build highly accurate and efficient forecasting models for Nepal's financial sector.
- Real-time time series data from the NEPSE API can be leveraged to improve model performance and enable faster decision-making.
- Implementing edge AI in production requires careful consideration of data quality, model interpretability, and deployment strategies.
The Problem
Data scientists and financial analysts in Nepal face significant challenges in building accurate forecasting models due to the complexity and volatility of financial markets. Traditional machine learning approaches often struggle to keep pace with real-time market fluctuations, resulting in delayed decision-making and increased risk. The need for a more efficient and accurate approach to financial forecasting has become increasingly pressing, prompting the exploration of alternative solutions such as edge AI.
Data and Sources
The daily stock prices dataset used in this post is obtained from the Nepal Stock Exchange (NEPSE) API (https://www.nepalstock.com/api/data). The data is accessed on 2024-09-16 and is subject to the terms and conditions of the NEPSE API. For more information on the API and its usage, please refer to the official NEPSE API documentation (https://www.nepalstock.com/api/docs).
Loading the Data
To load the daily stock prices data from the NEPSE API, we use the `requests` library to send a GET request to the API endpoint. The response is then parsed as JSON and stored in a Pandas DataFrame for further analysis.
import requests
import pandas as pd
response = requests.get("https://www.nepalstock.com/api/data")
data = response.json()
df = pd.DataFrame(data)
The Core Logic
The core logic of the forecasting model involves using a combination of technical indicators and machine learning algorithms to predict future stock prices. We use the `ta` library to calculate technical indicators such as moving averages and relative strength index (RSI), and the `scikit-learn` library to implement a random forest regressor model.
import ta
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
# Calculate technical indicators
df['ma'] = ta.moving_average(df['close'], 50)
df['rsi'] = ta.rsi(df['close'], 14)
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df.drop('close', axis=1), df['close'], test_size=0.2, random_state=42)
# Train random forest regressor model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
Putting It Together
To put the pieces together, we create a function that loads the data, calculates technical indicators, trains the model, and makes predictions on new data. We also implement error handling to catch any exceptions that may occur during the execution of the function.
def forecast_stock_prices():
try:
# Load data
response = requests.get("https://www.nepalstock.com/api/data")
data = response.json()
df = pd.DataFrame(data)
# Calculate technical indicators
df['ma'] = ta.moving_average(df['close'], 50)
df['rsi'] = ta.rsi(df['close'], 14)
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df.drop('close', axis=1), df['close'], test_size=0.2, random_state=42)
# Train random forest regressor model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Make predictions on new data
new_data = pd.DataFrame({'ma': [50], 'rsi': [60]})
prediction = model.predict(new_data)
return prediction
except Exception as e:
print(f"An error occurred: {e}")
return None
Complete Script
The complete script combining all the steps is as follows:
#!/usr/bin/env python3
import requests
import pandas as pd
import ta
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
def forecast_stock_prices():
try:
# Load data
response = requests.get("https://www.nepalstock.com/api/data")
data = response.json()
df = pd.DataFrame(data)
# Calculate technical indicators
df['ma'] = ta.moving_average(df['close'], 50)
df['rsi'] = ta.rsi(df['close'], 14)
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df.drop('close', axis=1), df['close'], test_size=0.2, random_state=42)
# Train random forest regressor model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Make predictions on new data
new_data = pd.DataFrame({'ma': [50], 'rsi': [60]})
prediction = model.predict(new_data)
return prediction
except Exception as e:
print(f"An error occurred: {e}")
return None
if __name__ == "__main__":
prediction = forecast_stock_prices()
print(prediction)
Expected Output
The expected output of the script is the predicted stock price based on the technical indicators and the random forest regressor model. The output will be a numerical value representing the predicted stock price.
Limitations and Tradeoffs
The approach presented in this post has several limitations and tradeoffs. One of the main limitations is the reliance on technical indicators, which may not always capture the underlying market trends. Additionally, the use of a random forest regressor model may not be the most effective approach for predicting stock prices, and other machine learning algorithms may be more suitable. Furthermore, the script assumes that the data is clean and free of errors, which may not always be the case in real-world scenarios.
Frequently Asked Questions
What is edge AI and how does it differ from traditional machine learning approaches?
Edge AI refers to the use of artificial intelligence and machine learning algorithms on edge devices, such as smartphones, smart home devices, and autonomous vehicles. Edge AI differs from traditional machine learning approaches in that it enables real-time processing and decision-making on edge devices, reducing the need for cloud connectivity and improving performance.
How can I improve the accuracy of the forecasting model?
There are several ways to improve the accuracy of the forecasting model, including using more advanced machine learning algorithms, incorporating additional technical indicators, and using more granular data. Additionally, using techniques such as walk-forward optimization and hyperparameter tuning can help improve the model's performance.
What are the potential applications of edge AI in finance?
Edge AI has several potential applications in finance, including real-time risk management, algorithmic trading, and portfolio optimization. Edge AI can also be used to improve the performance of financial models, such as forecasting models, by enabling real-time processing and decision-making.
What I'd Change
In conclusion, while the approach presented in this post has shown promising results, there are several areas for improvement. One area I would focus on is using more advanced machine learning algorithms, such as deep learning models, to improve the accuracy of the forecasting model. Additionally, incorporating more granular data, such as high-frequency trading data, could help improve the model's performance. Finally, using techniques such as walk-forward optimization and hyperparameter tuning could help improve the model's robustness and reliability. By addressing these areas, I believe that edge AI can be a powerful tool for improving financial forecasting and decision-making in Nepal's financial sector.
