Backtest Like a Pro with a Forex API

The dynamic nature of financial markets necessitates utilizing reliable data to develop and validate trading strategies. Efficiently incorporating high-quality data within backtesting environments is crucial for traders and analysts. TraderMade APIs empower these professionals by providing precise, detailed, and comprehensive market data.

This analysis leverages TraderMade's Time Series API to obtain historical data, execute a straightforward Simple Moving Average (SMA) crossover strategy, and evaluate its historical performance.

About SMA Crossover Strategy

The Simple Moving Average (SMA) Crossover Strategy is a fundamental technical analysis technique. It involves the observation of two SMAs: a short-term SMA, which exhibits higher sensitivity to price shifts, and a long-term SMA, which mitigates the impact of short-term price volatility.

A buy signal is generated when the short-term SMA surpasses the long-term SMA, signifying a potential upward trend. Conversely, a sell signal is triggered when the short-term SMA falls below the long-term SMA, indicating a potential downward trend.

Data Collection

Start by installing TraderMade's SDK as follows:

!pip install tradermade

We employ the installed Software Development Kit (SDK) to retrieve hourly time series data for foreign exchange (forex) pairs. The subsequent Python code exemplifies obtaining data for the EUR/USD currency pair.

import tradermade as tm

import pandas as pd

def fetch_forex_data(api_key, currency, start_date, end_date,    interval="hourly", fields=["open", "high", "low", "close"]):



   # Set API key

   tm.set_rest_api_key(api_key)

   # Fetch the data

   data = tm.timeseries(currency=currency, start=start_date, end=end_date, interval=interval, fields=fields)



   # Convert data directly to DataFrame

   df = pd.DataFrame(data)



   # Convert 'date' column to datetime

   df["date"] = pd.to_datetime(df["date"])



   # Set 'date' as the index

   df.set_index("date", inplace=True)



   return df



# Adjust as needed

api_key = "YOUR TRADERMADE API KEY"

currency = "EURUSD"

start_date = "2024-11-01-00:00"

end_date = "2024-11-27-05:12"



# Fetch the data and display the first few rows

forex_data = fetch_forex_data(api_key, currency, start_date, end_date)

forex_data = forex_data.rename(columns={"open": "Open", "high": "High", "low": "Low", "close": "Close"})

forex_data.head()

Data acquisition and preprocessing for backtesting have been successfully completed.

Implementation and Backtesting of a Simple SMA Crossover Strategy

This section utilizes the backtesting Python library to define and evaluate our SMA crossover strategy. For those unfamiliar with the backtesting library, it is considered a prominent and robust Python framework for backtesting technical trading strategies. These strategies encompass a diverse range, including SMA crossover, RSI crossover, mean-reversal strategies, momentum strategies, and others.

import numpy as np

from backtesting import Backtest, Strategy

from backtesting.lib import crossover

from backtesting.test import SMA



# Define the SMA crossover trading strategy

class SMACrossoverStrategy(Strategy):

       def init(self):

           # Calculate shorter-period SMAs for limited data

           price = self.data.Close

           self.short_sma = self.I(SMA, price, 20)  # Short window

           self.long_sma = self.I(SMA, price, 60)  # Long window



       def next(self):

           # Check for crossover signals

           if crossover(self.short_sma, self.long_sma):

               self.buy()

           elif crossover(self.long_sma, self.short_sma):

               self.sell()



   # Initialize and run the backtest

bt = Backtest(forex_data, SMACrossoverStrategy, cash=10000, commission=.002)

result = bt.run()



   # Display the backtest results

print("Backtest Results:")

print(result)

The strategy employs two moving averages: a 20-period and a 60-period SMA. A buy order is executed when the short-term SMA surpasses the long-term SMA. Conversely, a sell order is triggered when the short-term SMA falls below the long-term SMA. Within a 25-day trading period, this straightforward strategy yielded a profit of $243 through six trades.

Equity and SMAs Curve Analysis

The subsequent Python code assesses the performance of the SMA crossover strategy. SMAs facilitate the visualization of price trends and identify crossover points that generate buy/sell signals. The equity curve serves as a performance metric, illustrating the impact of these signals on portfolio growth.

By integrating both curves, traders can readily observe the correlation between crossover events and changes in portfolio value, providing crucial insights into the efficacy of the SMA crossover strategy.

Plotly is utilized to visualize the equity and SMAs curves, enabling traders to evaluate their strategy's profitability effectively.

import plotly.graph_objects as go



# Plot equity curve and SMAs using Plotly



import plotly.graph_objects as go



fig = go.Figure()



# Add equity curve

equity_curve = result['_equity_curve']

fig.add_trace(go.Scatter(

   x=equity_curve.index,

   y=equity_curve['Equity'],

   mode='lines',

   name='Equity Curve',

   yaxis='y1'  # Link to first y-axis

))



# Add price with SMAs on a secondary y-axis

fig.add_trace(go.Scatter(

   x=forex_data.index,

   y=forex_data['Close'],

   mode='lines',

   name='Close Price',

   yaxis='y2',  # Link to second y-axis

   line=dict(color='blue')

))

fig.add_trace(go.Scatter(

   x=forex_data.index,

   y=forex_data['Close'].rolling(window=20).mean(),

   mode='lines',

   name='Short SMA (20)',

   yaxis='y2',  # Link to second y-axis

   line=dict(color='orange', dash='dash')

))

fig.add_trace(go.Scatter(

   x=forex_data.index,

   y=forex_data['Close'].rolling(window=60).mean(),

   mode='lines',

   name='Long SMA (60)',

   yaxis='y2',  # Link to second y-axis

   line=dict(color='green', dash='dash')

))



# Customize layout with two y-axes

fig.update_layout(

   title="Equity Curve and SMAs (SMA Crossover Strategy)",

   xaxis_title="Date",

   yaxis_title="Portfolio Value ($)",  # For equity curve

   yaxis=dict(title="Equity ($)", side="left"),

   yaxis2=dict(title="Price and SMAs", overlaying="y", side="right"),  # For price and SMAs

   template="plotly_dark",

   showlegend=True

)



# Show the plot

fig.show()

Concluding Remarks

Successful backtesting necessitates accurate, high-frequency data, and TraderMade's APIs facilitate seamless integration. Regardless of your experience level – whether you are a novice exploring diverse strategies or an experienced analyst developing sophisticated models – the company's offerings provide the necessary tools.

Are you prepared to incorporate TraderMade's APIs into your workflow? Initiate your journey today and transform your concepts into reality.