What is Signal Provider Due Diligence?
Signal Provider Due Diligence is the process of checking, in a structured way, the factual basis behind a signal provider’s marketed or shared information. The goal is to separate what is measurable and comparable (inputs, rules, costs, and execution details) from what is variable or unverified (future market behavior, changing conditions, and incomplete disclosures).
A “worked example” means you actually apply the due diligence steps to a specific, fully described scenario. To keep it verifiable, the example must state every assumption: what data you start from, how trades are modeled, what fees are included, and what you do or do not attempt to predict.
How does a worked example work in practice?
Below is a worked, numerical scenario. It is not a recommendation to trade and it does not claim any expected profits. It only demonstrates how due diligence can be performed with consistent assumptions.
Scenario setup (assumptions)
Assume a signal provider states two items:
- A “signal” rule that generates trades: one trade per day.
- A performance table for 20 days.
To due-diligence the provider, you agree on fixed assumptions for the example:
- Trading is executed at the next available time after the signal is received.
- Each trade’s notional position size is the same.
- You model the spread and commissions as fixed per trade (for example, “total trading cost” per trade is assumed and applied consistently).
- You do not assume future market conditions match the past; you only test internal consistency of the provider’s reported calculations against the assumptions.
Verification inputs you request (what to check)
In due diligence, you try to obtain or reconstruct:
- The exact trade direction and timing rules used to generate signals.
- The entry and exit methodology (market order vs. limit order), or at least a consistent rule.
- The cost model: whether spreads, commissions, and swap/financing are included.
- The position sizing method: fixed lot size, fixed risk per trade, or something else.
Worked calculation example (one consistent way to check)
Assume the provider claims the following for 20 trades (daily):
- Claimed net return before costs: +2.0% over 20 days.
- Assumed number of trades: 20.
You select your own transparent cost assumptions for the worked example:
- Cost per trade (spread + commission): 0.08% of notional.
- Total cost across 20 trades: 20 × 0.08% = 1.6%.
Now you apply a consistent calculation framework:
- Claimed net return after costs (based on your cost assumption) = +2.0% − 1.6% = +0.4%.
Then you compare this to the provider’s published “after costs” performance. If the provider shows a different after-cost number, the mismatch can be due to different cost inclusion (for example, swap included for some days), different timing (slippage), or different position sizing.
The key point is not whether +0.4% is “right” in reality (we are not using live market data). The key point is that the example shows the structure: you state cost assumptions and calculate an outcome that can be checked against the provider’s numbers.
Evidence and limitations: what can go wrong?
A worked example should also include material failure modes, because due diligence is limited by available information and modeling choices.
Limitation 1: unknown execution quality
Even if a provider shows historical results, actual execution can differ due to latency, order type, and slippage. If your worked example assumes “next available time” and “fixed costs,” but the provider’s table implicitly used different execution conditions, comparisons become unreliable.
Limitation 2: incomplete cost disclosure
Some performance summaries exclude certain costs. For due diligence, that means a “net” number may not be apples-to-apples. A workable check is to identify which cost categories are included and which are not, and then redo your calculations under clearly stated assumptions.
Limitation 3: survival bias and changing behavior
Historical performance does not establish future results. A provider’s strategy rules or risk limits may change, and market regimes change. Due diligence can check internal consistency, but it cannot guarantee predictive accuracy.
Limitation 4: model dependence
In the example above, you assumed a fixed cost per trade and a consistent position size.