Strategy Simulation with No Market Data

In an ideal world, traders and strategists would have access to a lot of market data before making decisions.

But reality often presents a different scenario. 

New markets, new products, or unprecedented situations force us to operate in a data vacuum. 

For instance, if you’re trying to backtest a certain product – inflation-linked bonds, crypto, a certain derivatives strategy – these types of things may not have long histories and you don’t have a lot of data to test.

This doesn’t mean we’re flying blind.

But it may mean we need to get more creative with our approach to strategy simulation.

We give an overview of how to operate plus a compiled list of tips at the bottom.

We also give an example of how to approach this issue to simulate a certain type of trading product.

Key Takeaways – Strategy Simulation with No Market Data

• Leverage Economic Assumptions
  • Base your strategy on core financial principles like risk-return dynamics.
• Develop Proxy Metrics
  • When direct data is unavailable, create proxies using similar assets or market behaviors.
• Generate Synthetic Data
  • Simulate realistic market data using the underlying properties of whatever you’re trying to quantitatively model.
• Stress Test for Extremes
  • Design scenarios around major risks, such as liquidity crises, to test strategy resilience.
• Focus on Robustness
  • Prioritize strategies that perform consistently across different scenarios rather than optimizing for specific outcomes.
• Example
  • We simulate the price movement/returns of inflation-indexed bonds.
• Compiled Tips
  • We compile all our potential workarounds when you’re lacking data and what to do about it.

Fundamental Economic Assumptions

Even without specific market data, certain financial principles hold true:

• Risk-return relationships
• Supply and demand dynamics
• Arbitrage constraints
• Regulatory capital requirements

Build your simulation framework on bedrock principles that apply. 

Each assumption beyond whatever is definitively known requires careful justification and sensitivity testing.

Creating Financial Proxy Metrics

When direct market data isn’t available, develop proxy indicators:

• For new derivatives, model based on underlying asset behavior and the nature of the derivative
• For emerging market assets, analyze similar markets at comparable development stages
• For novel trading strategies, look at analogous strategies in different asset classes

The key is finding financial relationships that parallel your target scenario and focus on the cause-effect relationships.

Simulation Techniques for Financial Markets

Synthetic Time Series Generation

Create artificial but plausible market data:

• Use known statistical properties of similar assets
• Incorporate stylized facts about financial markets:
  • Volatility clustering
  • Fat-tailed distributions
  • Mean reversion tendencies, if applicable (e.g., interest rates)
• Calibrate to available economic indicators

This synthetic data provides a testbed for strategy development.

Stress Testing Without Historical Precedent

Design extreme scenarios that could impact your strategy:

• Systematic risk events
• Liquidity crises
• Technology disruptions

For each scenario, model:

• Price impact
• Volume changes
• Correlation shifts
• Execution constraints

Quantitative Techniques

Bayesian Probability Frameworks

• Probabilistic forecasts
• Use Bayesian updating to refine estimates as minimal data becomes available
• Develop ensemble models combining multiple views

Account for common biases, such as overconfidence and anchoring.

Agent-Based Market Simulation

Model market dynamics through simulated participant behavior:

• Create agents representing different market participants:
  • Retail traders (i.e., tend to be more momentum focused)
  • Long-term investors (pensions that mostly stick to fixed allocations and will add/trim when it deviates, central banks who buy/sell for policy objectives)
• Assign realistic behavioral rules to each agent type
• Run simulations to see emergent market behavior

This approach can reveal potential market dynamics that you won’t get from theoretical pricing models.

Iterative Strategy Testing

Paper Trading with Synthetic Data

Test strategies in simulated environments:

• Generate multiple synthetic market scenarios
• Execute strategies with realistic constraints:
  • Transaction costs
  • Liquidity limits
  • Execution latency
• Analyze performance across scenarios

Focus on robustness rather than optimization to avoid overfitting to synthetic data.

Minimal Capital Live Testing

When possible, test strategies with minimal real capital:

• Start with small position sizes
• Focus on learning market dynamics rather than generating returns
• Gradually increase exposure as you gather real data

Use tight risk limits and clear exit criteria for these tests.

Tools for Managing Financial Uncertainty

Options Theory for Strategy Design

Apply options thinking to strategy development:

• Identify strategic “options” in your approach:
  • The option to scale up
  • The option to pivot to different assets
  • The option to pause or exit
• Roughly value these options using minimal assumptions
• Design strategies that preserve valuable options

This approach helps balance potential upside with downside protection.

Robust Optimization Techniques

Design strategies that perform adequately across many scenarios:

• Define a range of possible market conditions
• Optimize for the worst-case and average-case performance
• Use regularization to prevent overfitting
• Focus on risk-adjusted metrics rather than absolute returns

The goal is strategies that are “good enough” in many scenarios rather than optimal in one.

Example

Let’s say we wanted to simulate the price movement and returns of an inflation-linked bond over the next 10 years, while also including inflation payments at regular intervals.

Below is an example of Python code that generates synthetic data to do this.

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

np.random.seed(42)

# Parameters

years = 10

months = years * 12

initial_price = 1000 # Initial price of the bond

inflation_rate_mean = 0.03 # 3% average annual inflation rate

inflation_rate_std = 0.02 # standard deviation of 2% for inflation rate

coupon_rate = 0.015 # 1.5% coupon rate

time_steps = months

inflation_payment_interval = 12 # Payments once a year

# Monthly inflation rates and sim inflation-linked bond prices

inflation_rate_monthly = np.random.normal(inflation_rate_mean / 12, inflation_rate_std / np.sqrt(12), time_steps)

inflation_cumulative = np.cumprod(1 + inflation_rate_monthly) - 1 # Cumulative inflation over time

bond_prices = initial_price * (1 + inflation_cumulative)

# Coupon payments adjusted for inflation

coupon_payments = []

for i in range(1, time_steps + 1):

    ifi%inflation_payment_interval==0:

        coupon_payments.append(bond_prices[i-1] *coupon_rate)

    else:

        coupon_payments.append(0)

# DataFrame to hold the data

dates = pd.date_range(start="2025-01-01", periods=time_steps, freq='M')

df = pd.DataFrame({

    'Date': dates,

    'Bond Price': bond_prices,

    'Coupon Payments': coupon_payments

})

# Plot the bond prices and coupon payments

plt.figure(figsize=(10, 6))

plt.plot(df['Date'], df['Bond Price'], label='Bond Price', color='b')

plt.plot(df['Date'], df['Coupon Payments'], label='Coupon Payments', color='g', linestyle='--')

plt.title('Simulated Inflation-Linked Bond Prices and Coupon Payments (Next 10 Years)')

plt.xlabel('Date')

plt.ylabel('Price/Payments')

plt.legend()

plt.grid(True)

plt.tight_layout()

# Show plot

plt.show()

# Display the dataframe for reference

import ace_tools as tools; tools.display_dataframe_to_user(name="Inflation-Linked Bond Data", dataframe=df)

Compiled List of Tips

Here’s a compiled list of tips and principles to help:

Synthetic Data Generation

When data is lacking, you sometimes have to generate your own.

The major pro to this is that you can generate as much as you want unlike the oft-limited real world.

The downside is that it might not have fidelity to the real world unless you do it carefully.

So you have to carefully specify what exactly you want the data to look like.

Statistical models like GARCH can capture time-varying volatility in asset prices, which can offer a more realistic simulation of markets.

Also, machine learning techniques such as Generative Adversarial Networks (GANs) can generate synthetic financial time series that mimic complex market behaviors to help improve the robustness of your simulations.

Monte Carlo Simulations

Monte Carlo methods allow you to simulate a wide range of possible market conditions by randomly sampling from probability distributions of key variables.

This not only diversifies the scenarios considered but also helps in evaluating the probability of extreme losses.

It can help you stress test under various market shocks.

Monte Carlo is a staple of financial modeling simulation.

Leverage Transfer Learning

Applying models and insights from well-established markets to new ones can provide important starting points, especially when adjusted for specific differences.

Developing adaptive models that learn from minimal data by transferring knowledge from related domains accelerates the modeling process and improves predictive accuracy.

For instance, if you’re trying to learn more about inflation-linked bonds, you can first start with the behavior of nominal bonds of similar qualities (e.g., issuer, duration) and go from there.

Integrate Behavioral Finance

Accounting for trader psychology helps you model market anomalies caused by human behaviors like herding and overreaction (e.g., why individual traders tend to be more momentum-based relative to, e.g., pension funds).

Introducing agents with varying risk preferences and decision-making heuristics into simulations creates more realistic market dynamics.

In turn, this can reflect the diversity of real-world participants.

Bayesian Networks

Bayesian networks enable you to model probabilistic relationships between economic indicators and asset prices systematically.

For example, if discounted growth, discounted inflation, risk premiums, and discount rates change by X, how does that change the behavior of Y asset class?

As new data becomes available, these networks can be dynamically updated, continuously refining predictions and enhancing model relevance over time.

Optimization Techniques

Optimizing strategies for worst-case scenarios is done so you have a portfolio that’s resilient under the worst possible market conditions.

Optimizing for average performance can create drawdowns or tail risk that isn’t acceptable.

Techniques like robust regression minimize the impact of outliers or model misspecification, which can lead to more reliable strategy outcomes.

Regime-Switching Models

Modeling different market states – such as bull, bear, or high volatility regimes – and allowing transitions between them captures the dynamic nature of financial markets.

Expand Agent-Based Modeling

Using machine learning within agents means you can adjust strategies based on market outcomes, which adds more adaptability to your simulations.

Simulating network effects, like information flow and social influence, helps you understand how interactions between agents impact overall market dynamics.

We give some basic agent-based models here.

Conduct Sensitivity and Scenario Analysis

Systematically varying key model parameters allows for understanding their impact on strategy performance.

Designing custom scenarios that reflect potential future events relevant to your market or asset prepares you for specific risks and unknowns.

Risk Management Practices

Using metrics like Value at Risk (VaR), Expected Shortfall (ES), Tail VaR, and Conditional VaR quantifies potential losses and informs risk limits.

They can provide clearer insight into potential downsides.

Regular stress testing evaluates how strategies hold up under extreme but plausible conditions.

Feedback Mechanisms

Develop strategies that learn and improve over time through reinforcement learning, which enables adaptation.

Many have complained about reinforcement learning’s ineffectiveness in finance, but it’s all about application.

Data Augmentation Techniques

Creating additional synthetic datasets through bootstrapping and resampling increases simulation robustness by expanding data for testing.

Introducing controlled noise into your data tests the resilience of strategies against imperfections and real-world irregularities.

Domain Experts

If possible, having financial analysts or market practitioners review your models (if that’s what they specialize in) provides valuable expert validation.

Combining quantitative models with qualitative insights from experts captures nuances that purely data-driven models might miss.

Visualization

Visuals can make it easier to identify patterns and anomalies.

Three-dimensional graphs can represent complex relationships between multiple variables and provide deeper insights into interdependencies within your models.

Plan for Data Acquisition

Develop a plan to collect real market data as it becomes available rather that relying purely on synthetic data.

This can help validate and refine your models over time.

Leveraging publicly available data from whatever sources possible can serve as useful proxies in the meantime.

Modular Frameworks

Design your simulation framework modularly so components can be updated or replaced without overhauling the entire system.

Modular components can also be reused for different assets or markets, increasing efficiency and scalability.

Computational Efficiency

As your system gets larger, you may need to optimize your simulation algorithms for speed and resource usage.

This may not be important for one-off sims or analyses, but can certainly be important when running large-scale simulations.

Using parallel computing techniques allows multiple simulations to run simultaneously and can significantly reduce computational time.

Document Assumptions and Limitations

Keeping detailed records of all assumptions, methodologies, and potential limitations promotes transparency and facilitates future revisions.

Allowing others to review and critique your models can help identify blind spots or errors.

Prepare for Real-World Deployment

Before full deployment, conduct pilot tests in controlled environments to observe how strategies perform with real market frictions.

Evaluate how strategies hold up as you scale position sizes or enter multiple markets to make sure they’re ready for broader application.

Conclusion

Operating without market data is challenging but not impossible. Success requires:

• Rigorous application of financial principles
• Creative approaches to generating testable scenarios
• A focus on robustness over a range of scenarios/environments over optimization
• Continuous learning and adaptation

Every major financial innovation started without historical data. 

Those who can effectively navigate the unknowns often find the greatest opportunities. 

Balance analytical rigor with the humility to know what you don’t know.