SIPWise

SIPWise – Goal-Based SIP Investment Predictor

SIPWise started with a simple thought:
"Why do traditional SIP calculators ignore volatility, risk, and the actual behavior of markets?"

Most tools ask you to input a goal amount, duration, and expected return and then spit out a number. But anyone who's seen market charts knows returns are never linear, and risk is real. I wanted to fix that.

Why SIPWise?

SIPWise doesn't rely on flat average returns or ideal-case scenarios. It simulates real-world volatility, accounts for risk preferences, and uses historical asset data to tell you how much you really need to invest monthly to reach your goal with confidence.

It's not just a calculator it's a model-driven financial planner.

Data Collection & Cleaning

It all began with sourcing raw historical data overlapping CSVs from Kaggle for Bitcoin (BTC-INR), Gold, and Nifty50. For Fixed Deposits (FD), I assumed a steady 6% annual return as a baseline.

Once I aligned all the timeframes to monthly closing prices, I had a clean dataset ranging from 2007 to 2021 enough to model a full business cycle with bull runs, crashes, and corrections.

Calculating Yearly Returns

Next, I resampled the monthly data into yearly data and calculated annual returns using:

Annual Return = (Price_end - Price_start) / Price_start

This gave me a clean dataframe of yearly percentage returns a crucial base for understanding long-term asset behavior.

CAGR – Compound Annual Growth Rate

To model compounding over time, I used the classic CAGR formula:

CAGR = (Final Value / Initial Value)^(1 / Years) - 1

What emerged was eye-opening:

• Bitcoin: ~170%
• Gold: ~10%
• Nifty50: ~6.3%

The huge spread highlighted how different risk profiles could drastically alter outcomes.

Volatility – Standard Deviation of Returns

Returns are only half the story. I wanted to quantify risk so I computed the standard deviation (σ) of returns for each asset:

σ = sqrt( Σ (Rᵢ - R̄)² / (N - 1) )

This revealed how erratic or stable each asset had been year-to-year.

Sharpe Ratio – Risk-Adjusted Returns

To understand which assets offered good returns per unit of risk, I calculated the Sharpe Ratio:

Sharpe Ratio = (Return - Risk-Free Rate) / Volatility

Assuming a risk-free rate of 4%, this gave me the lens to compare Bitcoin's wild growth vs FD's stability.

Defining Risk Profiles

With all metrics in place, I created 3 sample profiles Conservative, Balanced, and Aggressive with intuitive asset allocations:

risk_profiles = {
  'Conservative': {'FD': 0.60, 'Gold': 0.30, 'Nifty50': 0.10, 'Bitcoin': 0.00},
  'Balanced':     {'FD': 0.30, 'Gold': 0.30, 'Nifty50': 0.40, 'Bitcoin': 0.00},
  'Aggressive':   {'FD': 0.00, 'Gold': 0.20, 'Nifty50': 0.60, 'Bitcoin': 0.20}
}

This made SIPWise flexible, users can pick a profile that suits their appetite for risk.

Modeling Portfolio Returns & Volatility

Once the risk profiles were defined, I computed the expected return and volatility for each of them using historical asset data.

Portfolio Return
Calculated as a weighted sum of the individual asset CAGRs:

Portfolio Return = w₁ * CAGR₁ + w₂ * CAGR₂ + ... + wₙ * CAGRₙ

Where:

• wᵢ = weight of asset i in the portfolio
• CAGRᵢ = compound annual growth rate of asset i

Portfolio Volatility
To make it realistic, I accounted for how asset returns move together, using a covariance matrix.

Portfolio Volatility (σ_p) = √(wᵗ ⋅ Σ ⋅ w)

Where:

• w = vector of asset weights (e.g., [0.3, 0.3, 0.4])
• Σ = covariance matrix of asset returns

Note: Fixed Deposits (FD) were excluded from the volatility computation since they have near-zero fluctuation.

This step ensured SIPWise would suggest not just aggressive or conservative plans blindly, but ones that were rooted in real historical risk-adjusted behavior.

Simulating Investment Growth with Monte Carlo

With the math for expected returns and volatility in place, I wanted to make the predictions feel real, not just ideal-case scenarios.

That's where Monte Carlo Simulation came in.

Instead of assuming fixed growth every year, I modeled how investments actually grow, through ups and downs, by introducing randomness.

I simulated monthly portfolio growth using the formula:

R_month ~ 𝒩(r⁄12, σ⁄√12)

Where:

• r is the annual portfolio return
• σ is the annual volatility
• R_month is a randomly drawn return for the month

Each month, I:

1. Added the SIP amount
2. Applied a randomly drawn return
3. Repeated for the full investment duration (e.g., 5 years = 60 months)

I didn’t stop at one simulation, I ran this 20000+ times for each combination of goal, duration, and risk profile.

This gave me a distribution of final outcomes, some did better than expected, some worse. The average gave me the expected final portfolio value.

Why all this effort?

Because real-life investing isn’t linear, and I wanted SIPWise to reflect that truth.

Generating Synthetic Training Data

Now that I had a working simulation engine, I needed data, lots of it.

Since user input is typically:

• Goal Amount
• Duration (in years)
• Risk Profile

...I decided to reverse the problem.

Instead of:

“Here’s my SIP, tell me the final amount.”

I flipped it to:

“Here’s my goal, how much should I invest monthly to reach it?”

So, I wrote a loop to generate 20000+ synthetic data points by:

1. Randomly sampling:

   • Goal amount (e.g., ₹1L to ₹10L)
   • Duration (1 to 10 years)
   • Risk profile (Conservative, Balanced, Aggressive)

2. Running Monte Carlo simulations with varying SIPs

3. Finding the SIP that leads to the expected final value ≈ goal

For each data point, I stored:

• Monthly SIP
• Final portfolio value
• Risk profile
• Duration
• Asset weights

This gave me a clean training dataset, tailor-made for supervised learning.

Training the ML Model

With a solid dataset in hand, I moved on to training the model.

I chose a Random Forest Regressor, because:

• It handles non-linear relationships well
• It’s robust to outliers
• It performs well even with relatively small datasets

I trained the model using:

• Input features:

  • Goal amount
  • Duration (years)
  • Risk profile (one-hot encoded)
  • Asset weights (FD, Nifty50, Gold, Bitcoin)

• Output label:

  • Required Monthly SIP

After tuning hyperparameters and validating using cross-validation, the model performed surprisingly well, mean absolute error (MAE) was comfortably low across test cases.

And just like that, SIPWise had a brain!

Making Predictions

Once the model was trained, I built an interactive interface using Gradio. It allowed users to:

1. Enter their goal amount
2. Choose a duration in years
3. Select a risk profile

Behind the scenes, SIPWise:

• Fetches the asset allocation for the chosen risk profile
• Uses the trained Random Forest Regressor to predict the monthly SIP required
• Simulates portfolio growth using Monte Carlo for transparency
• Returns summary statistics: expected final value, average CAGR, and volatility

This wasn’t just a static calculator, it adapted dynamically to each user’s scenario, making financial planning feel personal, intelligent, and real.

Tech Stack

• Python
• Pandas
• NumPy
• Scikit-learn
• Gradio
• Hugging Face Spaces

Quant Techniques

• CAGR (Compound Annual Growth Rate)
• Volatility (Standard Deviation)
• Sharpe Ratio
• Covariance Matrix & Portfolio Volatility
• Monte Carlo Simulation

Conclusion

SIPWise is more than just a SIP calculator, it's a goal-based investment simulator that brings together financial data, volatility modeling, and machine learning to make smarter predictions.

Instead of assuming fixed returns, it learns from real-world Indian asset data (FDs, Nifty50, Gold, Bitcoin), uses Monte Carlo simulations to account for market ups and downs, and predicts how much you need to invest monthly to reach your goal based on your risk profile and investment horizon.

It was built out of curiosity and refined through countless experiments, making finance feel less rigid and more personal.

🔗 Live Demo: SIPWise
✨ GitHub Repo: SIPWise GitHub