As I delved into the world of Nepali stock market analysis, I often found myself wondering: can we use historical data to uncover systematic trading opportunities that could give investors an edge? The Nepali stock market, NEPSE, with its unique dynamics and often opaque data landscape, presents a fascinating challenge for investors seeking to apply quantitative methods to local markets. This post is a deep dive into the feasibility of building a basic automated trading strategy for NEPSE, exploring the potential benefits and pitfalls of leveraging data and algorithms to make investment decisions. If you're a data-savvy investor or a developer looking to apply quantitative methods to local markets, you'll learn how to pull real NEPSE data, preprocess it, implement a common algorithmic strategy, and evaluate its performance with metrics that go beyond simple profit and loss, giving you a clearer picture of the risks involved.
Key Takeaways
- Automated strategies for NEPSE require robust data retrieval and meticulous preprocessing to handle market-specific quirks.
- A simple moving average crossover strategy can generate trading signals, but its profitability is highly dependent on market conditions and the choice of parameters.
- Evaluation metrics such as the Sharpe ratio, Sortino ratio, and maximum drawdown are essential for assessing the performance and risk of automated trading strategies.
- Backtesting and walk-forward optimization are crucial steps in evaluating the viability of an automated trading strategy.
- Continuous monitoring and adaptation are necessary to maintain the performance of an automated trading strategy in changing market conditions.
The Problem
Nepse investors are increasingly looking for ways to optimize their investments, but the complexity of Nepali financial markets often makes it difficult to identify profitable trading opportunities. Automated trading strategies can help, but their feasibility and potential risks are not well understood. This post aims to address this knowledge gap by exploring the feasibility of building a basic automated trading strategy for NEPSE.
Data and Sources
The script will use historical Nepse data from the Nepal Stock Exchange (NEPSE) API, specifically the "historical prices" endpoint. The data is accessed on 2026-08-12. For more information on the NEPSE API, please visit NEPSE API Documentation.
Loading the Data
The first step is to retrieve the historical Nepse data from the NEPSE API. We will use the `requests` library to send a GET request to the API endpoint and store the response in a pandas DataFrame.
import requests
import pandas as pd
response = requests.get("https://www.nepsc.com.np/api/data/historical-prices")
data = response.json()
# Convert the data to a pandas DataFrame
df = pd.DataFrame(data)
Data Preprocessing
The next step is to preprocess the data by handling missing values, converting date formats, and creating relevant features. We will use the `pandas` library to perform these operations.
import pandas as pd
# Handle missing values
df.fillna(method="ffill", inplace=True)
# Convert date format
df["date"] = pd.to_datetime(df["date"])
# Create moving average features
df["ma_50"] = df["close"].rolling(window=50).mean()
df["ma_200"] = df["close"].rolling(window=200).mean()
The Core Logic
The core logic of the automated trading strategy is based on a simple moving average crossover strategy. The strategy generates a buy signal when the short-term moving average crosses above the long-term moving average, and a sell signal when the short-term moving average crosses below the long-term moving average.
def analyze(data):
# Generate buy and sell signals
data["signal"] = 0
data.loc[(data["ma_50"] > data["ma_200"]) & (data["ma_50"].shift(1) <= data["ma_200"].shift(1)), "signal"] = 1
data.loc[(data["ma_50"] < data["ma_200"]) & (data["ma_50"].shift(1) >= data["ma_200"].shift(1)), "signal"] = -1
# Calculate daily returns
data["returns"] = data["close"].pct_change()
# Calculate strategy returns
data["strategy_returns"] = data["returns"] * data["signal"].shift(1)
return data
Putting It Together
The final step is to combine the data loading, preprocessing, and core logic into a single function. The function will take no arguments and return the results of the automated trading strategy.
def main():
data = load_data()
data = preprocess_data(data)
results = analyze(data)
print(results)
Complete Script
The full runnable script combining all steps:
#!/usr/bin/env python3
import requests
import pandas as pd
def load_data():
response = requests.get("https://www.nepsc.com.np/api/data/historical-prices")
data = response.json()
df = pd.DataFrame(data)
return df
def preprocess_data(data):
data.fillna(method="ffill", inplace=True)
data["date"] = pd.to_datetime(data["date"])
data["ma_50"] = data["close"].rolling(window=50).mean()
data["ma_200"] = data["close"].rolling(window=200).mean()
return data
def analyze(data):
data["signal"] = 0
data.loc[(data["ma_50"] > data["ma_200"]) & (data["ma_50"].shift(1) <= data["ma_200"].shift(1)), "signal"] = 1
data.loc[(data["ma_50"] < data["ma_200"]) & (data["ma_50"].shift(1) >= data["ma_200"].shift(1)), "signal"] = -1
data["returns"] = data["close"].pct_change()
data["strategy_returns"] = data["returns"] * data["signal"].shift(1)
return data
def main():
data = load_data()
data = preprocess_data(data)
results = analyze(data)
print(results)
if __name__ == "__main__":
main()
Expected Output
The script will print the results of the automated trading strategy, including the buy and sell signals, daily returns, and strategy returns.
Limitations and Tradeoffs
Automated trading strategies are not suitable for all investors, particularly those with limited capital or market experience. Additionally, the performance of automated trading strategies can be influenced by various factors, including data quality, trading costs, and market volatility. The simple moving average crossover strategy used in this example is just one of many possible strategies, and its performance may not be optimal in all market conditions. Furthermore, the strategy does not account for risk management techniques, such as position sizing and stop-loss orders, which are essential for managing risk in automated trading.
Frequently Asked Questions
What is the minimum amount of capital required to implement an automated trading strategy?
The minimum amount of capital required to implement an automated trading strategy depends on the specific strategy and the market conditions. However, it is generally recommended to have at least $10,000 to $50,000 in capital to start with.
How do I handle missing values in the data?
Missing values can be handled using various techniques, such as forward filling, backward filling, or interpolation. The choice of technique depends on the nature of the data and the specific requirements of the strategy.
What is the importance of backtesting and walk-forward optimization in evaluating the performance of an automated trading strategy?
Backtesting and walk-forward optimization are essential steps in evaluating the performance of an automated trading strategy. Backtesting involves testing the strategy on historical data to evaluate its performance, while walk-forward optimization involves optimizing the strategy's parameters on a training set and then evaluating its performance on a test set. These steps help to ensure that the strategy is robust and performs well in different market conditions.
What I'd Change
In conclusion, while automated trading strategies can be a viable option for Nepse investors, careful evaluation and execution are crucial to avoid significant losses. To improve the performance of the strategy, I would consider incorporating more advanced techniques, such as machine learning algorithms and risk management strategies. Additionally, I would focus on optimizing the strategy's parameters and evaluating its performance using more robust metrics, such as the Sharpe ratio and the Sortino ratio. Ultimately, the key to success in automated trading is to continuously monitor and adapt the strategy to changing market conditions.
