Backtesting With Bid And Ask Prices In VectorBT

Hey guys! Are you diving into the world of algorithmic trading and backtesting? That's awesome! One of the coolest libraries out there to help you with this is VectorBT. It's super powerful and makes backtesting strategies a breeze. Today, we're going to explore how to use bid and ask prices within VectorBT to make your backtests even more realistic. Trust me, this is a game-changer!

Understanding Bid and Ask Prices

So, let's kick things off by understanding what bid and ask prices actually are. Imagine you're at a bustling marketplace, trying to buy or sell something. The bid price is the highest price a buyer is willing to pay for an asset, while the ask price is the lowest price a seller is willing to accept. The difference between these two prices is known as the bid-ask spread, and it represents the market's liquidity and the cost of executing a trade immediately.

In real-world trading, you can't always buy at the exact price you see on a chart. You might have to pay the ask price when buying and receive the bid price when selling. Ignoring this spread in your backtests can lead to overly optimistic results. After all, backtesting is about simulating real-world trading as closely as possible, and that includes accounting for the bid-ask spread. If your backtests don't reflect these real-world costs, you might end up with a strategy that looks amazing on paper but falls flat when you deploy it live. We want to avoid that, right? We want our backtests to be as realistic and reliable as possible, so we can have confidence in our strategies when we put them to the test in the live market.

Why is this crucial for accurate backtesting? Well, the bid-ask spread directly impacts your entry and exit prices. When you enter a long position, you typically buy at the ask price, which is slightly higher than the current market price. Conversely, when you exit a long position, you sell at the bid price, which is slightly lower. These small differences can add up, especially if you're trading frequently or in volatile markets. By incorporating bid and ask prices into your backtests, you're essentially adding a layer of realism that helps you better understand the true performance of your strategy. It's like adding friction to a physics simulation – it makes the results more grounded and less idealized. This leads to a more accurate assessment of your strategy's profitability and risk, which is exactly what we're aiming for in backtesting. Ignoring these spreads can paint a rosy picture that doesn't hold up in live trading, potentially leading to costly surprises. So, let's make sure our backtests are as true to life as possible!

Why Incorporate Bid and Ask Prices in Backtesting?

Incorporating bid and ask prices into backtesting is crucial for a few key reasons. Primarily, it provides a more realistic simulation of trading costs. When you execute a trade in the real world, you're not just buying or selling at the mid-price; you're interacting with the bid-ask spread. This spread represents the transaction cost, and ignoring it can significantly skew your backtesting results.

Think of it this way: imagine you're backtesting a high-frequency trading strategy that makes dozens or even hundreds of trades per day. Even a small bid-ask spread can eat into your profits significantly over that many trades. If your backtest doesn't account for this, you might think your strategy is highly profitable when, in reality, the transaction costs could wipe out a large portion of your gains. By including bid and ask prices, you get a much clearer picture of your strategy's true profitability after accounting for these costs. It's like checking your bank statement after all the fees and charges have been applied – you get the real story, not just the idealized version. This level of accuracy is essential for making informed decisions about whether to deploy a strategy in live trading.

Moreover, bid-ask spreads can vary depending on market conditions and the liquidity of the asset you're trading. During periods of high volatility or low liquidity, the spread can widen, making it more expensive to execute trades. A backtest that doesn't consider these variations might overestimate your strategy's performance during such periods. For instance, during a market crash or a surprise news event, bid-ask spreads can spike dramatically, potentially triggering stop-loss orders at unfavorable prices. If your backtest doesn't simulate these conditions, you might be in for a rude awakening when these events occur in live trading. By incorporating bid and ask data, you can assess how your strategy performs under different market conditions and adjust your parameters accordingly. This helps you build a more robust strategy that can withstand the ups and downs of the market.

By including bid and ask prices, you also gain a better understanding of your strategy's slippage. Slippage refers to the difference between the expected price of a trade and the actual price at which it's executed. This can happen due to market volatility, order size, or other factors. A realistic backtest should account for slippage, and using bid and ask prices is a great way to do that. It's like testing your brakes on a wet road versus a dry one – you need to see how your strategy performs under less-than-ideal conditions. In the end, incorporating bid and ask prices into your backtesting workflow is a critical step towards building more reliable and profitable trading strategies. It's all about getting as close to reality as possible in your simulations, so you can make informed decisions and manage your risk effectively when the real money is on the line.

Setting Up Your Backtest with VectorBT

Okay, let's get practical! To use bid and ask prices in VectorBT, you'll need to have the historical bid and ask data for the assets you're trading. There are several data providers that offer this, such as IEX Cloud, Alpaca, and Polygon.io. Once you have the data, you can load it into your backtesting environment.

First things first, you'll need to import the necessary libraries. Make sure you have VectorBT installed (pip install vectorbt). Then, you can import it along with pandas, which is super handy for data manipulation:

import vectorbt as vbt
import pandas as pd

Next, you'll load your bid and ask data. Assuming you have separate CSV files for bid and ask prices, you can use pandas to read them into DataFrames:

bid_price = pd.read_csv('bid_prices.csv', index_col='timestamp', parse_dates=True)
ask_price = pd.read_csv('ask_prices.csv', index_col='timestamp', parse_dates=True)

Make sure your DataFrames have a datetime index, as this is crucial for VectorBT to align the data correctly. If your timestamp column has a different name, you can adjust the index_col parameter accordingly. Now that you have your bid and ask price data loaded, you need to integrate it into your VectorBT backtesting framework. This involves ensuring that VectorBT uses these prices when simulating trade execution, rather than just relying on the mid-price (the average of bid and ask). One common approach is to adjust your entry and exit signals to account for the bid-ask spread. For example, when entering a long position, you would use the ask price to calculate your entry price, and when exiting, you would use the bid price. This reflects the real-world costs of trading and gives you a more accurate picture of your strategy's performance. VectorBT allows you to customize the price series used for different aspects of your backtest, so you can precisely control how bid and ask prices are incorporated. This level of flexibility is one of the reasons why VectorBT is so powerful for realistic backtesting. It lets you fine-tune your simulations to match the nuances of actual market conditions, giving you a much better understanding of how your strategy will perform in the real world.

Implementing Bid and Ask Prices in VectorBT

Now, let's dive into the core of the matter: how to actually implement bid and ask prices within VectorBT. In the function you mentioned for portfolio metrics:

pf = vbt.Portfolio.from_signals(
    signal_data['close'],
    entries,
    exits,
    init_cash=...
)

You're using the close price. To incorporate bid and ask, you'll need to adjust this. VectorBT's Portfolio.from_signals function can accept different price series for entries and exits. This is where the magic happens!

Instead of passing signal_data['close'] directly, you'll pass the ask price for entries and the bid price for exits. This simulates buying at the ask and selling at the bid, which is exactly what happens in real trading. So, you'll modify your function call like this:

pf = vbt.Portfolio.from_signals(
    close=signal_data['close'],
    entries=entries,
    exits=exits,
    entry_price=ask_price,
    exit_price=bid_price,
    init_cash=...
)

Here, we've added entry_price=ask_price and exit_price=bid_price. This tells VectorBT to use the ask price when calculating the cost of entering a position and the bid price when calculating the proceeds from exiting a position. It's a simple change, but it makes a huge difference in the realism of your backtest. It's like upgrading from a basic calculator to a scientific one – you get a much more accurate and detailed result. This is crucial for strategies that are sensitive to transaction costs, such as high-frequency trading or strategies that involve frequent entries and exits. By using bid and ask prices, you can see exactly how the bid-ask spread impacts your profitability and adjust your strategy accordingly. It's all about fine-tuning your approach to maximize your returns and minimize your risk.

One thing to keep in mind is that the close parameter is still there. This is used for calculating other metrics and can be any price you prefer, such as the mid-price or the close price. The key is that the entry_price and exit_price parameters are what determine the actual prices used for trade execution. Another important aspect to consider is the alignment of your data. VectorBT relies on the index (typically the timestamp) to align the price series and signals. If your bid, ask, and signal data have different timestamps or frequencies, you might need to resample or align them before passing them to Portfolio.from_signals. Pandas provides powerful tools for resampling and aligning time series data, so you can easily ensure that your data is consistent and compatible. For example, you might want to resample your bid and ask prices to the same frequency as your close prices, or you might need to fill in missing data points using techniques like forward fill or backward fill. By paying attention to these details, you can avoid common pitfalls and ensure that your backtest is as accurate and reliable as possible. It's all about the details when it comes to backtesting, and getting the data alignment right is a critical step.

Handling Slippage and Transaction Costs

While using bid and ask prices helps simulate transaction costs, you might also want to consider slippage. Slippage is the difference between the expected price of a trade and the actual price at which it's executed. This can happen due to market volatility or order size.

VectorBT doesn't have a direct parameter for slippage, but you can simulate it by adding a small percentage or fixed amount to the ask price for entries and subtracting it from the bid price for exits. This is a common technique for incorporating slippage into backtests and can help you get an even more realistic view of your strategy's performance.

For example, you could add a 0.1% slippage factor to the ask price and subtract it from the bid price. This would simulate the effect of your orders being filled at slightly worse prices than expected, which is a common occurrence in real trading. To implement this, you would modify your entry_price and exit_price calculations like this:

slippage_factor = 0.001  # 0.1%
entry_price_with_slippage = ask_price * (1 + slippage_factor)
exit_price_with_slippage = bid_price * (1 - slippage_factor)

pf = vbt.Portfolio.from_signals(
    close=signal_data['close'],
    entries=entries,
    exits=exits,
    entry_price=entry_price_with_slippage,
    exit_price=exit_price_with_slippage,
    init_cash=...
)

This simple addition can significantly improve the accuracy of your backtest, especially for strategies that rely on precise entry and exit points. It's like adding a margin of error to your calculations – it helps you account for the unexpected variations that can occur in the market. Another aspect to consider is the impact of transaction costs, such as brokerage fees or commissions. VectorBT allows you to specify a commission per trade, which can be a fixed amount or a percentage of the trade value. This is another way to make your backtest more realistic and to ensure that you're accurately assessing your strategy's profitability. To add a commission, you can use the комиссии parameter in Portfolio.from_signals. For example, if you want to simulate a commission of $1 per trade, you would add комиссия =1 to your function call. By incorporating both slippage and transaction costs into your backtest, you're taking a comprehensive approach to simulating the real-world costs of trading. This will give you a much clearer picture of your strategy's true performance and help you make informed decisions about risk management and capital allocation.

Analyzing Backtesting Results

Once you've run your backtest with bid and ask prices, it's time to analyze the results. VectorBT provides a wealth of metrics that can help you evaluate your strategy's performance, such as total return, ** Sharpe ratio**, maximum drawdown, and more.

Pay close attention to how these metrics change when you incorporate bid and ask prices. You'll likely see a reduction in your total return and Sharpe ratio, as the transaction costs eat into your profits. However, this is a more realistic representation of your strategy's performance. It's better to have a slightly lower but more accurate return than an inflated return that doesn't hold up in live trading.

To access these metrics, you can use the pf.stats() method, which returns a pandas Series containing various performance statistics:

stats = pf.stats()
print(stats)

This will give you a comprehensive overview of your strategy's performance, including key metrics like total return, annual return, Sharpe ratio, maximum drawdown, and more. By comparing the stats from backtests with and without bid and ask prices, you can clearly see the impact of transaction costs on your strategy's profitability. This is valuable information for refining your strategy and optimizing your parameters. For example, you might find that a strategy that looks great on paper without bid and ask prices becomes less attractive when these costs are taken into account. This might lead you to adjust your trading frequency, position sizing, or other parameters to improve your strategy's risk-adjusted returns. In addition to the basic performance metrics, VectorBT also provides tools for analyzing your strategy's trades, such as the average trade duration, the win rate, and the profit factor. These metrics can give you deeper insights into how your strategy is performing and where there might be room for improvement. For instance, if you find that your strategy has a low win rate but a high profit factor, it might be worth exploring ways to increase your win rate, even if it means sacrificing some profitability per trade. Similarly, if you find that your average trade duration is very short, you might want to consider the impact of transaction costs on your strategy's overall performance. By using VectorBT's comprehensive suite of analysis tools, you can gain a thorough understanding of your strategy's strengths and weaknesses and make data-driven decisions about how to optimize it.

Conclusion

Incorporating bid and ask prices into your VectorBT backtests is a crucial step towards creating more realistic and reliable trading strategies. It helps you account for transaction costs and slippage, providing a more accurate view of your strategy's performance. So, next time you're backtesting, remember to use those bid and ask prices, guys! Happy trading!

By using bid and ask prices, you're not just making your backtests more accurate; you're also gaining a deeper understanding of how your strategy will perform in the real world. This knowledge empowers you to make better decisions, manage your risk more effectively, and ultimately, improve your trading results. It's like having a clearer picture of the road ahead – you can navigate the twists and turns with more confidence and avoid potential pitfalls. So, take the time to incorporate bid and ask prices into your backtesting workflow, and you'll be well on your way to becoming a more successful algorithmic trader. And remember, backtesting is an iterative process. Don't be afraid to experiment with different parameters, analyze your results, and make adjustments to your strategy as needed. The more you practice and refine your approach, the better your chances of developing a winning trading system. So, keep learning, keep testing, and keep improving – the rewards are well worth the effort!