How does this DCA strategy differ from regular DCA?

Regular DCA uses a fixed amount every period. This MCP uses valuation percentiles to increase investment amounts when assets are cheap and decrease them when they are expensive.

What AI clients can I use with this MCP?

You can use any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code.

Do I need to host the MCP myself?

No, Vinkius hosts and manages the MCP for you. You just connect your client and start using the capabilities.

Can I use this to compare my strategy to a lump-sum investment?

Yes, the performance metrics include comparisons to lump-sum returns.

What kind of data do I need to provide for a simulation?

You typically provide a series of historical prices and corresponding valuation metrics like P/E ratios.

How does the valuation adjustment work?

The strategy uses the get_valuation_percentile logic to scale investments. If the valuation is in the bottom 25th percentile, it invests 1.5x the base amount. Between 25th and 75th, it invests 1.0x. Above 75th, it invests 0.5x, and above 90th, it stops investing (0x).

Can I compare my DCA results to a lump-sum investment?

Yes, the simulate_dca_strategy capability provides a lumpSumReturnPercent metric, which compares the DCA performance against investing the total planned amount at the initial price point.

What inputs are required for a simulation?

To use simulate_dca_strategy, you need a price series, a corresponding valuation series (like P/E ratios), the base investment amount, and the investment frequency (e.g., 'weekly' or 'monthly').

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Connect Dollar Cost Averaging Strategy Simulator to your AI

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Test smarter investment models using historical valuation data.

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Dollar-Cost Averaging Strategy.

01
Simulate dca strategy
This capability runs a complete simulation of a DCA strategy that adjusts buy amounts based on valuation percentiles.
02
Calculate performance metrics
This capability computes the final financial outcomes, including total returns and average costs, for a completed investment period.
03
Get valuation percentile
This capability determines where a specific valuation point sits within a historical dataset.

Real work

What teams actually run

Testing Smart DCA

You can simulate a strategy that buys more when P/E ratios are low to see if it beats a standard monthly buy.

Portfolio Stress Testing

Run simulations over volatile historical periods to see how valuation-based rules handle market swings.

Market Entry Analysis

Use the percentile capability to see if current market valuations are historically cheap or expensive.

Performance Benchmarking

Compare the results of your custom DCA strategy against a lump-sum investment of the same total amount.

One connector, every AI

Dollar-Cost Averaging Strategy works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Dollar-Cost Averaging Strategy to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

955ms average. Fast in production.

Dollar-Cost Averaging Strategy is checked daily against the live service.

Daily averagePeak 1134ms

Oct 3Today

Fastest day: 729ms
Slowest day: 1134ms
14-day trend: Improving-25%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Dollar-Cost Averaging Strategy, so you can see the experience inside your AI.

It does not authenticate your account with Dollar-Cost Averaging Strategy. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Dollar-Cost Averaging Strategy Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_o2yhrrIGJCWI4TAAEFhtyMAUXZHknQmvZX3yvFJM/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — Dollar-Cost Averaging Strategy capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "dollar-cost-averaging-strategy-simulator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_o2yhrrIGJCWI4TAAEFhtyMAUXZHknQmvZX3yvFJM/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Dollar-Cost Averaging Strategy in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Dollar-Cost Averaging Strategy owners hand off.

This MCP is built for people who want to mathematically model investment behaviors rather than just tracking prices.

01
Quantitative Analysts
They hand off historical data to the agent to test specific mathematical models.
02
Individual Investors
They use the agent to simulate how different DCA rules would have affected their portfolio.
03
Financial Researchers
They use the capabilities to compare valuation-based strategies against standard benchmarks.

FAQ

Questions Dollar-Cost Averaging Strategy owners ask.

01
How does this DCA strategy differ from regular DCA?
Regular DCA uses a fixed amount every period. This MCP uses valuation percentiles to increase investment amounts when assets are cheap and decrease them when they are expensive.
02
What AI clients can I use with this MCP?
You can use any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code.
03
Do I need to host the MCP myself?
No, Vinkius hosts and manages the MCP for you. You just connect your client and start using the capabilities.
04
Can I use this to compare my strategy to a lump-sum investment?
Yes, the performance metrics include comparisons to lump-sum returns.
05
What kind of data do I need to provide for a simulation?
You typically provide a series of historical prices and corresponding valuation metrics like P/E ratios.
06
How does the valuation adjustment work?
The strategy uses the get_valuation_percentile logic to scale investments. If the valuation is in the bottom 25th percentile, it invests 1.5x the base amount. Between 25th and 75th, it invests 1.0x. Above 75th, it invests 0.5x, and above 90th, it stops investing (0x).
07
Can I compare my DCA results to a lump-sum investment?
Yes, the simulate_dca_strategy capability provides a lumpSumReturnPercent metric, which compares the DCA performance against investing the total planned amount at the initial price point.
08
What inputs are required for a simulation?
To use simulate_dca_strategy, you need a price series, a corresponding valuation series (like P/E ratios), the base investment amount, and the investment frequency (e.g., 'weekly' or 'monthly').

Explore

More in Finance

Connect Dollar-Cost Averaging Strategy to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-03

Simulate dca strategy

Calculate performance metrics

Get valuation percentile

What teams actually run

Testing Smart DCA

Portfolio Stress Testing

Market Entry Analysis

Performance Benchmarking

Building your own app? The connector is yours to use.

Dollar-Cost Averaging Strategy Connector

Claude Desktop

Quantitative Analysts

Individual Investors

Financial Researchers

How does this DCA strategy differ from regular DCA?

What AI clients can I use with this MCP?

Do I need to host the MCP myself?

Can I use this to compare my strategy to a lump-sum investment?

What kind of data do I need to provide for a simulation?

How does the valuation adjustment work?

Can I compare my DCA results to a lump-sum investment?

What inputs are required for a simulation?

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