Strategy research workspace
An AI trading bot workspace built for clearer decisions.
Write the idea down, turn it into a hypothesis you can test, and run it against labelled sample scenarios. Every result shows its inputs, assumptions and the reasoning behind the summary.
- Paper simulation only
- Synthetic sample data, clearly labelled
- Email sign-in, no password
Research note · draft
Sample data · Simulation- Strategy (after costs)
- Buy-and-hold
- Net return
- 18.44%
- Max drawdown
- 11.9%
- Trades
- 2
In the Volatility shock sample, the strategy finished at 18.44% after costs, versus 6.79% for simply holding (difference 11.65 percentage points).
Only 2 trades: too few to tell skill from chance. Treat any conclusion as a hypothesis to test on more scenarios.
Costs took 2.2% of gross gains at the assumed fee and slippage.
Rule-based explanation — not generated by an AI model
Change the scenario: the same rule can look smart or naive depending on the regime.
Raise the costs: watch how quickly a small edge disappears.
The research loop
Idea → hypothesis → test → conclusion
Most trading ideas are never tested because they are never written precisely enough to test. The notebook walks you through four short steps.
Write the idea in plain words
Capture what you noticed and why you think it matters — before any numbers.
“After a sharp drop, prices often keep falling for a while.”
Turn it into a testable hypothesis
Name the condition, the expected outcome, the horizon and what would prove you wrong.
IF the fast average crosses below the slow one THEN staying flat beats holding over 252 bars FAILS IF net result is worse than buy-and-hold in 3 of 4 scenarios
Test it on sample scenarios
Run the same rule across four synthetic market regimes with explicit fee, slippage and position-size assumptions.
Scenario: Volatility shock · Template: MA crossover 10/40 · Costs: 10 bps per round trip
Write down what you concluded
Read the rule-based summary, then record your own conclusion and the next question. The run is stored with its inputs.
“Helps in the shock scenario, costs it in the steady trend. Next: test a slower exit.”
Sample strategy, in detail
One rule, worked through on sample data
A moving-average crossover (10/40 bars) on the “Volatility shock” scenario, with 5 bps fee and 5 bps slippage per fill. Here is how to read each number.
MA crossover 10/40 · Volatility shock
Sample data · Paper trading- Strategy (after costs)
- Buy-and-hold
| Metric | Value | How to read it |
|---|---|---|
| Net return | 18.44% | Result after fees and slippage on a 10,000 sample account. |
| Buy-and-hold | 6.79% | What simply holding through the same bars would have returned. |
| Max drawdown | 11.9% | Largest fall from a peak — the stretch you would have had to sit through. |
| Trades | 2 | Number of completed round trips. Fewer than 10 is too few to judge. |
| Time in market | 53.2% | Share of bars with an open position; the rest of the time it was flat. |
| Costs / gross gains | 2.2% | How much of the gross gains the cost assumptions consumed. |
Why this example
Trend-following rules are often described as “protection” in sell-offs. The shock scenario lets you check that claim on data where the shock is known in advance.
What it shows
The rule steps aside after the gap but also gives back gains in the calm stretch before it. Whether that trade-off is acceptable is your decision, not the tool's.
What it cannot show
One synthetic path is one possibility. A real market adds gaps between bars, partial fills and costs that change with conditions.
In the Volatility shock sample, the strategy finished at 18.44% after costs, versus 6.79% for simply holding (difference 11.65 percentage points).
Only 2 trades: too few to tell skill from chance. Treat any conclusion as a hypothesis to test on more scenarios.
Costs took 2.2% of gross gains at the assumed fee and slippage.
The largest peak-to-trough fall was 11.9%.
Synthetic data with a simple execution model (next-bar open, fixed costs). A real market adds gaps, partial fills and changing costs.
Rule-based explanation — not generated by an AI model
What is in the workspace
Four parts, one research record
Strategy Notebook
Notes with a structured hypothesis, the scenario and template you tested, and every run you saved. Strategy Notebook
Saved to your account · export JSON / CSV / Markdown
Market Context
Four documented sample regimes — trend, range, volatility shock, drawdown and recovery — to test the same idea against. Market Context
Synthetic, fixed-seed data · not market prices
Paper Trading Workspace
Deterministic paper simulation with explicit fees, slippage and position size. Same inputs, same result. Paper Trading Workspace
No brokerage · no orders · no funds
Research Summaries
Plain-language statements derived from the numbers by published rules. Optional AI explanation when the operator enables it, always labelled. Research Summaries
Rules documented on the Methodology page
Market context
Same strategy, four market regimes
Pick a scenario. The rule and the costs stay the same; only the market changes. This is the single most useful habit in strategy research.
MA crossover 10/40 · Steady trend
Sample data- Net return
- 0.39%
- Buy-and-hold
- 22.59%
- Max drawdown
- 13.43%
- Time in market
- 57.5%
- Trades
- 5
In the Steady trend sample, the strategy finished at 0.39% after costs, versus 22.59% for simply holding (difference -22.2 percentage points).
Only 5 trades: too few to tell skill from chance. Treat any conclusion as a hypothesis to test on more scenarios.
Costs took 71.5% of gross gains (97.75 in the sample account). Small edges are fragile once fees and slippage are included.
The largest peak-to-trough fall was 13.43%.
Synthetic data with a simple execution model (next-bar open, fixed costs). A real market adds gaps, partial fills and changing costs.
Sample data generated with fixed seeds. Costs: 5 bps fee + 5 bps slippage per fill. Not a backtest on market prices.
Limits and control
What the tool does — and what stays with you
A research tool is only useful if its limits are as clear as its features.
Verdaxis does
- Structure ideas into testable hypotheses
- Simulate paper trades on labelled sample scenarios
- Show every input, assumption and formula
- Keep a dated record you can export or delete
Verdaxis does not
- Connect to a broker or exchange
- Hold money or place orders
- Use live or historical market prices
- Tell you what to buy or sell
You decide
- Which questions are worth testing
- What would count as failure
- Whether a result is good enough to keep studying
- What, if anything, you do with it outside the tool
What does Verdaxis do?
It is a research workspace: you turn a trading idea into a testable hypothesis, run paper simulations of simple strategy templates on labelled sample scenarios, and keep the results and your conclusions in a notebook.
Who is it for?
Self-directed people who have trading ideas and want a repeatable way to test and document them before spending more time or money on them.
Do I need trading or programming experience?
No programming. Some familiarity with basic market terms helps; every parameter has a short explanation, and the Methodology page explains every formula.
What do I get after confirming my email?
The Strategy Notebook: unlimited notes (up to 200), paper runs on the four sample scenarios, run history, export to JSON/CSV/Markdown and deletion of your data.
Is real money involved?
No. Verdaxis never connects to a brokerage or exchange, never holds funds and never places orders. Every result is a paper simulation.
What does “sample data” and “simulation” mean here?
The prices are synthetic, generated from a documented model with fixed seeds — they are not live or historical market prices. Simulation means the trades are calculated, not executed.
What can the AI part do, and what are its limits?
Summaries are rule-based by default and follow published thresholds. If the operator enables an AI provider, a model can add a prose explanation, labelled with its name. Neither predicts markets; both describe a simulation and can be incomplete or wrong.
What data do you store?
Your email address, your notes and runs, a session while you are signed in, and records of consents you gave. No passwords, payment details or IP addresses in our database.
How do I delete my data?
In the workspace, open Settings: delete your notes or the whole account. Deletion is immediate. You can also email the privacy address.
How do I unsubscribe from the newsletter?
Use the unsubscribe link at the bottom of any newsletter email, or switch it off in Settings. Your workspace access is not affected.
Get access
Open the research workspace
Confirm your email and the Strategy Notebook opens. No password, no payment details.
- Unlimited notes and paper runs on the four sample scenarios
- Run history, export and deletion of your own data
- Optional newsletter — separate, never required