An in-depth guide to fast-forward the process of building algo trading strategies
Introduction
The rise of Large Language Models (LLMs) has reshaped many industries and the financial markets are no exception. These models are currently deployed to analyze market trends, develop trading strategies, and execute trades with remarkable accuracy. Their deployment in finance has unlocked new opportunities for traders to make smarter, data-driven decisions. One of their main advantages is that they can process massive amounts of financial information in real time, helping them identify profitable opportunities with minimal to no risks.
In this article, we explore how EODHD’s custom GPT plugin can be deployed to pull stock data, perform some analysis, and build an effective trading strategy. This GPT is trained to provide accurate code for working with EODHD’s financial APIs. Our goal is to showcase the growing influence of AI in financial markets and analyze whether LLM-powered strategies can outperform traditional ones.
EODHD GPT: Background and Applications
EODHD GPT is a custom-built AI model designed specifically for financial data analysis and trading strategy development. It integrates historical and fundamental market data, news sentiment analysis, and technical indicators to provide insightful trading recommendations. This model can be utilized for various applications, including:
Real-time stock market analysis
Generating trading signals
Developing and backtesting algorithmic trading strategies
Evaluating risk and optimizing portfolio performance
Automating code generation for traders and investors
Traders can leverage the power of EODHD GPT to enhance their decision-making process, automate strategy execution, and gain a competitive advantage in the financial markets. The custom GPT acts as a code companion for traders reducing the time and effort needed to write trading algorithms manually. The following prompt asks the GPT to provide code for extracting historical AAPL stock data.
The GPT returns the following code that requires the user to change the API key from demo to the correct one.
import requests
import pandas as pd
# API endpoint and parameters
url = "https://eodhd.com/api/eod/AAPL.US"
params = {
'api_token': 'demo', # Using 'demo' key for AAPL
'from': '2023-01-01',
'to': '2024-12-31',
'period': 'd', # Daily data
'fmt': 'json' # JSON format
}
# Send the request
response = requests.get(url, params=params)
# Convert to DataFrame
data = response.json()
df = pd.DataFrame(data)
# Convert 'date' column to datetime
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
df.sort_index(inplace=True)
# Display first few rows
print(df.head())
# Optional: Save to CSV
df.to_csv('AAPL_Historical_Data_2023_2024.csv')
When the above code is run in a Python environment, we get the following results.
Preliminary Analysis: Stock Data Extraction and Trend Analysis
In this section, we prompt the GPT to provide a Python code that performs preliminary analysis. The analysis we are interested in includes basic descriptive statistics, a plot of close price trends, daily returns and visualization, volatility, moving averages, and correlation between OHLC (open, high, low, close). We use the following prompt:
The following code was obtained:
import pandas as pd
import matplotlib.pyplot as plt
# Load the previously saved data (or use df directly if in memory)
df = pd.read_csv('AAPL_Historical_Data_2023_2024.csv', parse_dates=['date'], index_col='date')
# --- 1. Basic Descriptive Statistics ---
print("Descriptive Statistics:\n")
print(df[['open', 'high', 'low', 'close', 'volume']].describe())
# --- 2. Plot Close Price Trend ---
plt.figure(figsize=(12, 6))
plt.plot(df.index, df['close'], label='Close Price', color='blue')
plt.title('AAPL Close Price Trend (2023-2024)')
plt.xlabel('Date')
plt.ylabel('Price ($)')
plt.legend()
plt.grid(True)
plt.show()
# --- 3. Daily Returns Calculation & Visualization ---
df['Daily_Return'] = df['close'].pct_change()
plt.figure(figsize=(12, 5))
plt.plot(df.index, df['Daily_Return'], label='Daily Return', color='green')
plt.title('AAPL Daily Returns (2023-2024)')
plt.xlabel('Date')
plt.ylabel('Return')
plt.legend()
plt.grid(True)
plt.show()
print("\nDaily Return Stats:\n")
print(df['Daily_Return'].describe())
# --- 4. Volatility Analysis (Rolling Std Dev) ---
df['Volatility'] = df['Daily_Return'].rolling(window=20).std() * 100 # Annualized % approx.
plt.figure(figsize=(12, 5))
plt.plot(df.index, df['Volatility'], label='20-day Rolling Volatility (%)', color='orange')
plt.title('AAPL Volatility (20-Day Rolling Std Dev)')
plt.xlabel('Date')
plt.ylabel('Volatility (%)')
plt.legend()
plt.grid(True)
plt.show()
# --- 5. Moving Averages ---
df['MA50'] = df['close'].rolling(window=50).mean()
df['MA200'] = df['close'].rolling(window=200).mean()
plt.figure(figsize=(12, 6))
plt.plot(df.index, df['close'], label='Close Price', color='black')
plt.plot(df.index, df['MA50'], label='50-Day MA', linestyle='--')
plt.plot(df.index, df['MA200'], label='200-Day MA', linestyle='--')
plt.title('AAPL Close Price with Moving Averages')
plt.xlabel('Date')
plt.ylabel('Price ($)')
plt.legend()
plt.grid(True)
plt.show()
# --- 6. Correlation Matrix ---
print("\nCorrelation Matrix:\n")
print(df[['open', 'high', 'low', 'close']].corr())
The descriptive statistics were as follows:
AAPL's close price trend shows a mix of upward and downward trends over the two years as follows.
The chart shows a clear bullish trend, especially noticeable after mid-2024 where the price accelerates upward. There are several pullbacks throughout, but each is followed by a recovery.
The daily returns were as follows:
There are noticeable periods with large positive or negative daily returns, particularly around early/mid-2024. The daily returns oscillate around zero, showing typical random walk behavior with volatility clusters.
The 20-day rolling volatility:
We note periods of elevated volatility, particularly visible leading up to the price dips. Also, we have lower volatility phases followed by expansions that hint at upcoming breakout moves.
Close price with moving averages:
The chart shows a 50-day MA crossing above the 200-day MA mid-2024, a classic bullish signal. One thing to note is that the price often finds support around the MA50 or MA200 before resuming upward movement.
The Process and Preliminary Analysis
This article covers the following aspects:
Extracts fundamental and historical AAPL stock data using EODHD custom GPT
Performs a preliminary analysis to ascertain the stock’s current and future trend
Using the GPT to suggest appropriate trading strategies based on the analysis
Generating and implementing these strategies
Backtesting the strategies using historical data
Evaluating and comparing the strategies to identify the best-performing one
Applying the best strategy to a buy-and-hold approach to test its viability
Preliminary Analysis
In this section, we prompt the GPT to extract historical data and perform basic analysis. The code should compute moving averages, RSI, MACD, and Bollinger Bands. The aim is to use these indicators to determine the stock’s trend — whether it is in an upward trend, downward trend, or trading sideways. This preliminary analysis helps in developing an appropriate trading strategy.
We test the custom GPT’s code generation by using the following prompt.
The custom GPT returned the following code:
import requests
import pandas as pd
import matplotlib.pyplot as plt
import ta # Technical Analysis Library
# Step 1: Get Historical EOD Data for AAPL from Jan 2023 to Dec 2024
url = "https://eodhd.com/api/eod/AAPL.US"
params = {
'api_token': 'demo',
'from': '2023-01-01',
'to': '2024-12-31',
'period': 'd',
'fmt': 'json'
}
response = requests.get(url, params=params)
data = response.json()
# Step 2: Convert data to DataFrame
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
df.sort_index(inplace=True)
# Step 3: Compute Moving Averages
df['MA50'] = df['close'].rolling(window=50).mean()
df['MA200'] = df['close'].rolling(window=200).mean()
# Step 4: Compute RSI
df['RSI'] = ta.momentum.RSIIndicator(close=df['close'], window=14).rsi()
# Step 5: Compute MACD
macd = ta.trend.MACD(close=df['close'])
df['MACD'] = macd.macd()
df['MACD_signal'] = macd.macd_signal()
# Step 6: Compute Bollinger Bands
bollinger = ta.volatility.BollingerBands(close=df['close'], window=20, window_dev=2)
df['Bollinger_High'] = bollinger.bollinger_hband()
df['Bollinger_Low'] = bollinger.bollinger_lband()
# Step 7: Visualization
plt.figure(figsize=(14, 8))
# Plot Close Price and Moving Averages
plt.subplot(3, 1, 1)
plt.plot(df.index, df['close'], label='Close Price', color='black')
plt.plot(df.index, df['MA50'], label='50-day MA', linestyle='--')
plt.plot(df.index, df['MA200'], label='200-day MA', linestyle='--')
plt.fill_between(df.index, df['Bollinger_High'], df['Bollinger_Low'], color='gray', alpha=0.3, label='Bollinger Bands')
plt.legend()
plt.title('AAPL Stock Price with Moving Averages and Bollinger Bands')
# Plot MACD
plt.subplot(3, 1, 2)
plt.plot(df.index, df['MACD'], label='MACD', color='purple')
plt.plot(df.index, df['MACD_signal'], label='Signal Line', color='orange')
plt.legend()
plt.title('MACD')
# Plot RSI
plt.subplot(3, 1, 3)
plt.plot(df.index, df['RSI'], label='RSI', color='green')
plt.axhline(70, color='red', linestyle='--')
plt.axhline(30, color='blue', linestyle='--')
plt.legend()
plt.title('RSI')
plt.tight_layout()
plt.show()
In the above code, we only need to use the correct API key instead of the ‘demo’ returned by the GPT. The following charts are rendered when we run the code in a Python environment.
Based on the chart above, we make the following key insights:
We observe a Golden Cross around mid-2024 where the 50-day moving average (MA50) crosses above the 200-day moving average (MA200). This means a strong bullish signal, indicating a shift towards an upward trend.
The price stays above the 50-day and 200-day MAs towards the end of 2024, suggesting a sustained bullish momentum.
Price Riding Upper Band (Late 2024) where the price touches or rides the upper Bollinger Band around October-December 2024
Multiple instances where the MACD line crosses above the signal line, particularly in early 2024 and mid-2024, signal buy opportunities
RSI frequently hits above 70 during late 2024, especially towards the year’s end. There’s a noticeable dip below 30 around August 2023, presenting a potential buying opportunity at that time.
Custom-GPT trading strategies
Based on the observations made, we request the custom GPT to provide a Python code that capitalizes on the observed insights. The following prompt was used to provide sufficient context to the model.
The GPT returned the following code:
import requests
import pandas as pd
import matplotlib.pyplot as plt
import ta # For indicators
# Step 1: Download historical data (Already done, but including for completeness)
url = "https://eodhd.com/api/eod/AAPL.US"
params = {
'api_token': 'demo',
'from': '2023-01-01',
'to': '2024-12-31',
'period': 'd',
'fmt': 'json'
}
response = requests.get(url, params=params)
data = response.json()
# Step 2: Data Preparation
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
df.sort_index(inplace=True)
# Step 3: Indicators Calculation
df['MA50'] = df['close'].rolling(window=50).mean()
df['MA200'] = df['close'].rolling(window=200).mean()
# RSI
df['RSI'] = ta.momentum.RSIIndicator(close=df['close'], window=14).rsi()
# MACD
macd = ta.trend.MACD(close=df['close'])
df['MACD'] = macd.macd()
df['MACD_signal'] = macd.macd_signal()
# Bollinger Bands
bollinger = ta.volatility.BollingerBands(close=df['close'], window=20, window_dev=2)
df['Bollinger_High'] = bollinger.bollinger_hband()
df['Bollinger_Low'] = bollinger.bollinger_lband()
# Step 4: Trading Strategy Implementation
df['Position'] = 0 # 1 = Long, 0 = No position
# Iterate rows to apply strategy
in_position = False
for i in range(1, len(df)):
# Entry - Golden Cross + Price above MA + MACD bullish
if (df['MA50'].iloc[i] > df['MA200'].iloc[i]) and \
(df['MA50'].iloc[i-1] <= df['MA200'].iloc[i-1]) and \
(df['close'].iloc[i] > df['MA50'].iloc[i]) and \
(df['MACD'].iloc[i] > df['MACD_signal'].iloc[i]):
df.at[df.index[i], 'Position'] = 1
in_position = True
# Early RSI Oversold Entry
elif df['RSI'].iloc[i] < 30 and not in_position:
df.at[df.index[i], 'Position'] = 1
in_position = True
# Add to Position: Riding Upper Bollinger + MACD bullish
elif in_position and (df['close'].iloc[i] >= df['Bollinger_High'].iloc[i]) and (df['MACD'].iloc[i] > df['MACD_signal'].iloc[i]):
df.at[df.index[i], 'Position'] = 1
# Exit Conditions
elif in_position:
# RSI overbought OR MACD crosses below signal OR Death Cross
if (df['RSI'].iloc[i] > 70) or \
(df['MACD'].iloc[i] < df['MACD_signal'].iloc[i]) or \
(df['MA50'].iloc[i] < df['MA200'].iloc[i] and df['MA50'].iloc[i-1] >= df['MA200'].iloc[i-1]):
df.at[df.index[i], 'Position'] = 0
in_position = False
else:
df.at[df.index[i], 'Position'] = 1
# Step 5: Plot the results
plt.figure(figsize=(14, 8))
plt.plot(df.index, df['close'], label='Close Price', color='black')
plt.plot(df.index, df['MA50'], label='50-day MA', linestyle='--')
plt.plot(df.index, df['MA200'], label='200-day MA', linestyle='--')
# Plot Buy/Sell Signals
buy_signals = df[(df['Position'] == 1) & (df['Position'].shift(1) == 0)]
sell_signals = df[(df['Position'] == 0) & (df['Position'].shift(1) == 1)]
plt.scatter(buy_signals.index, buy_signals['close'], marker='^', color='green', label='Buy', s=100)
plt.scatter(sell_signals.index, sell_signals['close'], marker='v', color='red', label='Sell', s=100)
plt.legend()
plt.title('Trading Strategy on AAPL Stock')
plt.show()
# Step 6: Optional - Calculate strategy returns
df['Returns'] = df['close'].pct_change()
df['Strategy_Returns'] = df['Returns'] * df['Position'].shift(1)
cumulative_returns = (1 + df['Strategy_Returns'].fillna(0)).cumprod()
print(f"Cumulative Strategy Return: {cumulative_returns.iloc[-1]:.2f}x initial capital")
The following image shows the model’s momentum-driven Golden Cross Strategy with RSI and Bollinger Filters. Its performance is as illustrated in the chart below.
The strategy shows a slight underperformance with a 2% loss from the initial capital.
Given the model’s underperformance, we request the GPT to provide another Python code that performs better than the previous strategy. The prompt is as follows:
The GPT returned the following Python code:
import requests
import pandas as pd
import matplotlib.pyplot as plt
import ta
# Step 1: Download historical data (AAPL)
url = "https://eodhd.com/api/eod/AAPL.US"
params = {
'api_token': 'demo',
'from': '2023-01-01',
'to': '2024-12-31',
'period': 'd',
'fmt': 'json'
}
response = requests.get(url, params=params)
data = response.json()
# Step 2: Prepare DataFrame
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
df.sort_index(inplace=True)
# Step 3: Indicators
df['MA200'] = df['close'].rolling(window=200).mean()
# ATR
atr = ta.volatility.AverageTrueRange(high=df['high'], low=df['low'], close=df['close'], window=14)
df['ATR'] = atr.average_true_range()
# MA200 Slope (Difference over 10 days)
df['MA200_slope'] = df['MA200'].diff(10)
# Entry & Exit Conditions
df['Position'] = 0
trailing_stop = None
entry_price = None
for i in range(20, len(df)):
# Entry Condition:
# - MA200 slope positive (trend filter)
# - Price > MA200 + 1.5 * ATR (volatility breakout filter)
if df['MA200_slope'].iloc[i] > 0 and \
df['close'].iloc[i] > df['MA200'].iloc[i] + 1.5 * df['ATR'].iloc[i] and \
df['Position'].iloc[i-1] == 0:
df.at[df.index[i], 'Position'] = 1
trailing_stop = df['close'].iloc[i] - 2 * df['ATR'].iloc[i]
entry_price = df['close'].iloc[i]
# Maintain Position
elif df['Position'].iloc[i-1] == 1:
# Update trailing stop if price increases
if df['close'].iloc[i] - 2 * df['ATR'].iloc[i] > trailing_stop:
trailing_stop = df['close'].iloc[i] - 2 * df['ATR'].iloc[i]
# Check trailing stop exit
if df['close'].iloc[i] < trailing_stop:
df.at[df.index[i], 'Position'] = 0
trailing_stop = None
else:
df.at[df.index[i], 'Position'] = 1
# Step 4: Plot
plt.figure(figsize=(14, 8))
plt.plot(df.index, df['close'], label='Close Price', color='black')
plt.plot(df.index, df['MA200'], label='200-day MA', linestyle='--')
# Buy & Sell Markers
buy_signals = df[(df['Position'] == 1) & (df['Position'].shift(1) == 0)]
sell_signals = df[(df['Position'] == 0) & (df['Position'].shift(1) == 1)]
plt.scatter(buy_signals.index, buy_signals['close'], marker='^', color='green', label='Buy', s=100)
plt.scatter(sell_signals.index, sell_signals['close'], marker='v', color='red', label='Sell', s=100)
plt.legend()
plt.title('ATR-Filtered Trend Rider Strategy on AAPL')
plt.show()
# Step 5: Returns
df['Returns'] = df['close'].pct_change()
df['Strategy_Returns'] = df['Returns'] * df['Position'].shift(1)
cumulative_returns = (1 + df['Strategy_Returns'].fillna(0)).cumprod()
print(f"Cumulative Strategy Return: {cumulative_returns.iloc[-1]:.2f}x initial capital")
The GPT provides an ATR trend rider strategy where it only takes long trades if MA200 is rising and uses Average True Range (ATR) to confirm breakouts. This strategy returns a 36% cumulative return compared to the previous that had a loss.
This strategy outperformed the previous one because trading occurred when the 200-day MA slope was rising, filtering out trades during sideways or weak markets (2023 period). Secondly, the ATR-based breakout condition ensures entries happen only when the price shows decisive movement, not random fluctuations. Thirdly, the ATR trailing stop allows capturing big uptrends (mid-2024 onward) without premature exits.
Given the two strategies, we backtest them using a buy-and-hold approach. We request the custom GPT to provide a code that compares the performance of the three strategies. The resulting code is as follows:
import requests
import pandas as pd
import matplotlib.pyplot as plt
import ta
# Step 1: Download AAPL Historical Data
url = "https://eodhd.com/api/eod/AAPL.US"
params = {
'api_token': 'demo',
'from': '2023-01-01',
'to': '2024-12-31',
'period': 'd',
'fmt': 'json'
}
response = requests.get(url, params=params)
data = response.json()
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
df.sort_index(inplace=True)
# Step 2: Indicators for both strategies
df['MA50'] = df['close'].rolling(window=50).mean()
df['MA200'] = df['close'].rolling(window=200).mean()
df['RSI'] = ta.momentum.RSIIndicator(close=df['close'], window=14).rsi()
macd = ta.trend.MACD(close=df['close'])
df['MACD'] = macd.macd()
df['MACD_signal'] = macd.macd_signal()
bollinger = ta.volatility.BollingerBands(close=df['close'], window=20, window_dev=2)
df['Bollinger_High'] = bollinger.bollinger_hband()
df['Bollinger_Low'] = bollinger.bollinger_lband()
atr = ta.volatility.AverageTrueRange(high=df['high'], low=df['low'], close=df['close'], window=14)
df['ATR'] = atr.average_true_range()
df['MA200_slope'] = df['MA200'].diff(10)
# Step 3: Strategy 1: Golden Momentum Strategy
df['Position_Golden'] = 0
in_position = False
for i in range(1, len(df)):
# Entry: Golden Cross + MACD
if (df['MA50'].iloc[i] > df['MA200'].iloc[i]) and (df['MA50'].iloc[i-1] <= df['MA200'].iloc[i-1]) and \
(df['close'].iloc[i] > df['MA50'].iloc[i]) and (df['MACD'].iloc[i] > df['MACD_signal'].iloc[i]):
df.at[df.index[i], 'Position_Golden'] = 1
in_position = True
# Early RSI Oversold Entry
elif df['RSI'].iloc[i] < 30 and not in_position:
df.at[df.index[i], 'Position_Golden'] = 1
in_position = True
# Exit Conditions
elif in_position:
if (df['RSI'].iloc[i] > 70) or (df['MACD'].iloc[i] < df['MACD_signal'].iloc[i]) or \
(df['MA50'].iloc[i] < df['MA200'].iloc[i] and df['MA50'].iloc[i-1] >= df['MA200'].iloc[i-1]):
df.at[df.index[i], 'Position_Golden'] = 0
in_position = False
else:
df.at[df.index[i], 'Position_Golden'] = 1
# Step 4: Strategy 2: ATR Trend Filtered Strategy
df['Position_ATR'] = 0
trailing_stop = None
for i in range(20, len(df)):
if df['MA200_slope'].iloc[i] > 0 and df['close'].iloc[i] > df['MA200'].iloc[i] + 1.5 * df['ATR'].iloc[i] and df['Position_ATR'].iloc[i-1] == 0:
df.at[df.index[i], 'Position_ATR'] = 1
trailing_stop = df['close'].iloc[i] - 2 * df['ATR'].iloc[i]
elif df['Position_ATR'].iloc[i-1] == 1:
if df['close'].iloc[i] - 2 * df['ATR'].iloc[i] > trailing_stop:
trailing_stop = df['close'].iloc[i] - 2 * df['ATR'].iloc[i]
if df['close'].iloc[i] < trailing_stop:
df.at[df.index[i], 'Position_ATR'] = 0
trailing_stop = None
else:
df.at[df.index[i], 'Position_ATR'] = 1
# Step 5: Buy & Hold Strategy
df['Position_Hold'] = 1 # Always holding
# Step 6: Calculate Returns
df['Returns'] = df['close'].pct_change()
df['Strategy_Returns_Golden'] = df['Returns'] * df['Position_Golden'].shift(1)
df['Strategy_Returns_ATR'] = df['Returns'] * df['Position_ATR'].shift(1)
df['Buy_Hold_Returns'] = df['Returns']
# Step 7: Cumulative Returns
df['Cumulative_Golden'] = (1 + df['Strategy_Returns_Golden'].fillna(0)).cumprod()
df['Cumulative_ATR'] = (1 + df['Strategy_Returns_ATR'].fillna(0)).cumprod()
df['Cumulative_BuyHold'] = (1 + df['Buy_Hold_Returns'].fillna(0)).cumprod()
# Step 8: Plot
plt.figure(figsize=(14, 8))
plt.plot(df.index, df['Cumulative_Golden'], label='Golden Momentum Strategy')
plt.plot(df.index, df['Cumulative_ATR'], label='ATR Trend Filtered Strategy')
plt.plot(df.index, df['Cumulative_BuyHold'], label='Buy & Hold Strategy')
plt.legend()
plt.title('Backtest Comparison: Strategies vs Buy & Hold')
plt.show()
# Step 9: Print Final Returns
print(f"Golden Momentum Strategy Return: {df['Cumulative_Golden'].iloc[-1]:.2f}x")
print(f"ATR Trend Filtered Strategy Return: {df['Cumulative_ATR'].iloc[-1]:.2f}x")
print(f"Buy & Hold Strategy Return: {df['Cumulative_BuyHold'].iloc[-1]:.2f}x")
Buy and hold strategy is where we enter a long position at the start and hold till the end. The following chart compares the performance of the three approaches.
Key takeaways from the comparison:
The buy-and-hold strategy was the best performer benefitting from a strong bull market period. It is a simple exposure without trying to time entries/exits outperforming active strategies. It maximized the compounding effect.
ATR trend filtered strategy performed moderately by relying on selective entries and trailing stops which provided protection but left gains on the table. It also avoided major drawdowns but underperformed when markets trended strongly.
The golden momentum strategy was an underperformer due to frequent whipsaws, early exits, and sensitivity to noise which led to flat or negative returns.
Custom-GPT hybrid system
Having confirmed that the GPT’s strategies were suboptimal, we prompt it to implement a hybrid approach, capitalizing on the strengths of each. The prompt is as follows:
The hybrid strategy performs as follows:
The hybrid system matches the buy and hold strategy due to the ATR% threshold (4%) and price < MA50 condition which might rarely trigger, especially in strong uptrend markets like AAPL during 2023–2024.
Secondly, since AAPL was in a clear strong bullish trend, volatility was present but not extreme enough (as per ATR%) to cause significant exposure reduction. These results show that with additional customization, the GPT can provide a competitive trading strategy.
Conclusion
An analysis of the three approaches — golden momentum, ATR filtered, and Buy and hold — underscores the significance of balancing risk management with market participation.
The EODHD’s custom GPT plugin helps traders effortlessly extract historical stock data, analyze it, and develop robust trading strategies. Although the buy-and-hold strategy demonstrated strong performance in trending markets, integrating adaptive risk controls like ATR can help protect capital during volatile periods.
The main takeaway is the custom GPT comes in handy for those without in-depth coding knowledge but are keen to make data-driven decisions to maximize returns in financial markets.
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