What
A transparent AI research platform
iPulse does not publish one opaque model answer. It compares many AI Agents, surfaces agreement and disagreement, and turns that into structured, inspectable research.
Methodology
iPulse Methodology: What, How, and Who
This page is the public technical map of iPulse. It explains what the platform actually does, how consensus scoring works, and how transparent AI research is structured.
Published July 2, 2026 · Updated July 2, 2026
How
Consensus-driven scoring and ranking
Assets are processed through multiple analyst frameworks, converted into normalized forecast signals, then ranked with consistency and volatility controls so noisy upside is not mistaken for conviction.
Who
Built by operators, not a copy desk
The platform is designed by builders with practical experience across investment research, AI systems, cloud-scale delivery, and go-to-market execution.
HERE IS HOW iPulse WORKS UNDER THE HOOD
Many AI Agents.One Transparent Consensus.
iPulse100+ Analysis Frameworks5,000+ AI SETUPS EXECUTED IN PARALLEL
Runs each individual asset (stock, crypto, commodity etc.) through analysis frameworks across best AI models in the world. Aggregates all reports of an Asset into a single Pulse Consensus Score. Ranks all assets from best to worst. Users can inspect the latest and all historical reports. Full Trust.
Average User
Asks Claude for deep research on stocks to buy. Doesn't know which investment framework is used for analysis. Can not guarantee AI is aware of latest macro-economic events. Cannot track past performance. No visuals for inspection. No Trust.
Control Animation
Queue
Bitcoin
NVIDIA
Gold
Apple
Alphabet
1. Asset Enters iPULSE Pipeline
Coca-Cola
News
Events
Earnings
Dividends
Filings
Reports
CEO
Market data
2. Swarm of AI Advisors Produce Independent Investment Reports
Advisors exist in many different configurations of tools, setups and contexts.
100+
Advisors
WarrenBuffett
MichaelBurry
ElonMusk
RayDalio
Machiavelli
Configured on Multiple Models:
Preparing advisor questions
Waiting for the first advisor to light up.
3. iPULSE Consensus Analysis
iPULSE
Algorithms compare independent advisors reports and aggregates them into one iPulse Consensus Rating.
4. Detailed forecast report
Coca-Cola reasoning, forecast path and score drivers.
Forecast+7.8%
iPulse Consensus Score+240
SellNeutralBuy
5. Top Picks updates
Signals become a ranked watchlist of what to buy or sell.
Rank #4
#1BitcoinSTRONG BUY+580
#2NVIDIASTRONG BUY+420
#3AlphabetSTRONG BUY+330
#4
Coca-ColaBUY+240
iPulse Consensus Score -- Formula and Methodology
How the ranking signal is computed
The score is not a promise. It is a disciplined compression of return direction, return magnitude, dividend income, historical volatility, analyst agreement, forecast-path stability, and economic guardrails into a single index from -1000 to +1000.
Current scoring version
v7.3 uses a 3.6% cash hurdle, capped MAD discount, 35%/65% signal blend, high-volatility return-anchor scaling, buy-side safety checks, and a sell-side economic floor.
Simple-language version
- 1. Start with each AI Advisor's forecast. iPulse asks what return the prediction implies over the selected horizon.
- 2. Add dividends into the canonical return path. For dividend-paying assets, expected income is folded into total return before ranking; price-only comparison is optional when ex-div diagnostics exist.
- 3. Put every forecast on the same annual scale. A 1-year forecast and a 5-year forecast can be compared without mixing timeframes.
- 4. Measure excess return above cash. The first hurdle is 3.6%, so low positive returns do not automatically become bullish.
- 5. Discount fragile consensus. Return MAD reduces excess-return magnitude before scoring, capped at 60% of excess return.
- 6. Balance risk and return size. The score blends historical-volatility awareness with a return-anchor term that scales down very high-volatility assets.
- 7. Reward agreement and penalize messy paths. Consistency can lift clean consensus; the volatility penalty dampens unstable forecast paths.
- 8. Keep signals economically honest. Weak bullish returns can be downgraded to NEUTRAL, and negative terminal economics stay visible as at least PARTIALLY SELL.
01
Convert each prediction into an annualized total-return voice
Input: One AI prediction's price path, forecast horizon, and dividend yield estimate (when applicable).
Radj,i = (((1 + Rbase,i/100) * (1 + y)Y) - 1) * 100
Ri = (((1 + Radj,i/100)1/Y) - 1) * 100
Ri = (((1 + Radj,i/100)1/Y) - 1) * 100
Output to next step
R, the annualized total-return voice for one prediction.
02
Group voices so one source cannot drown out the rest
Input: All annualized voice returns for the asset, grouped by selected mode: prediction, AI Advisor, or AI Persona.
Rg = mean(Ri in group g)
Output to next step
R_g, the grouped annualized return for each independent voice bucket.
03
Discount return spread and measure excess return over cash
Input: Consensus annualized return, cash hurdle, and return MAD across grouped voices.
E = R - 3.6%
Rconf = sign(E) * max(0, |E| - min(0.35 * MAD, 0.60 * |E|))
Rconf = sign(E) * max(0, |E| - min(0.35 * MAD, 0.60 * |E|))
Output to next step
R_conf, the confidence-adjusted excess return used for direction scoring.
04
Blend risk-aware return with return magnitude
Input: R_conf plus the asset's historical volatility and high-volatility return-anchor multiplier.
S = 0.35 * Rconf / max(sigmahist, 5%) + 0.65 * Rconf / (15% * Mvol)
Output to next step
S, the normalized direction signal before tanh bounding.
05
Turn direction into consensus strength and agreement
Input: The consensus signal S and the distribution of grouped voice signals.
b = tanh(S / 2)
consistency = e-sigmaS
consistency = e-sigmaS
Output to next step
b, the bounded direction; consistency, the agreement multiplier.
06
Dampen unstable forecast paths only after a proportional dead zone
Input: Consensus return magnitude and cross-voice forecast dispersion.
deadZone = clamp(0.5 * |R|, 1%, 10%)
volatilityPenalty = 0 when sigma <= deadZone
volatilityPenalty = min(1, ((sigma - dz) / (15 - dz))2) * 0.15
volatilityPenalty = 0 when sigma <= deadZone
volatilityPenalty = min(1, ((sigma - dz) / (15 - dz))2) * 0.15
Output to next step
volatilityPenalty, the instability discount applied to the final score.
07
Calculate the final score, then apply economic guardrails
Input: Direction, consistency, volatility penalty, annualized return, and return spread quality.
Score = round(b * (0.85 + 0.15 * consistency) * (1 - volatilityPenalty) * 1000)
BUY guard: R < 3.6% or (R - MAD) < -1% => NEUTRAL
Sell guard: R < -0.5% => at least PARTIALLY SELL
BUY guard: R < 3.6% or (R - MAD) < -1% => NEUTRAL
Sell guard: R < -0.5% => at least PARTIALLY SELL
Output to next step
A -1000 to +1000 iPulse score and final rating band.
Symbol dictionary
- R_base,i
- price-only compounded return for one prediction voice from the generation anchor price
- y
- annual net dividend yield, estimated from 3-year historical cash-dividend events when usable
- Y
- forecast horizon in years
- R
- annualized total return after dividend uplift (or zero uplift when no usable dividend history exists)
- E
- excess return above the 3.6% cash hurdle
- R_conf
- confidence-adjusted excess return after the capped MAD discount
- MAD
- mean absolute dispersion of grouped annualized returns, used to discount fragile upside
- sigma_hist
- 10-year historical annualized volatility, floored at 5%
- M_vol
- high-volatility denominator multiplier, 1.0x to 2.0x
- alpha
- 0.35 Sharpe weight; 0.65 return-anchor weight
- b
- bounded direction signal after tanh compression with k=2
- sell guard
- minimum sell-side visibility when annualized economic return is below -0.5%
Dividend handling
Leaderboard ranking uses dividend-inclusive total return as the canonical basis. Price-only comparison is shown only when ex-div diagnostic fields are available for that snapshot.
The yield is estimated from recent cash-dividend history, usually a 3-year window, using dividend amounts relative to prior close prices. Assets with no usable dividend history receive no dividend uplift.
Dividend uplift is most meaningful for 1-year and longer horizons. It can change both displayed annualized return and ranking score because income is treated as part of total shareholder return.
Agreement, dispersion, and recompute shocks
Consistency measures how tightly grouped the AI Advisor signals are after voice grouping. A narrow signal distribution receives a higher consistency term; a wide distribution reduces conviction.
Small deviations are ignored. Only genuinely unstable forecast paths reduce the score in a meaningful way.
Recomputed leaderboard snapshots compare the current market price with the prediction path. Post-analysis shocks below 12% are not dampened. Larger shocks attenuate the score input toward the 3.6% cash hurdle with capped horizon weights of 0.50 for 1Y, 0.30 for 3Y, and 0.20 for 5Y. Close prices and displayed return fields remain raw market/mechanical values. The raw pre-shock return is still used for the sell-side guard, so a below-current terminal forecast cannot hide as NEUTRAL.
One Score per AI Prediction
Every individual prediction counts as an equal vote. This is the raw, unadjusted signal.
One Score per AI Advisor
Predictions from the same AI Advisor are averaged before scoring, so one advisor cannot dominate simply by producing more forecasts.
One Score per AI Persona
Predictions sharing the same analytical persona are averaged into one vote per perspective. This is the most diversity-aware view.
Performance-weighted mode
Reserved for later use once enough outcome history exists to weight advisor votes by measured forecasting performance.
Rating bands
STRONG BUY
> +300
BUY
+131 to +300
NEUTRAL
-30 to +130
PARTIALLY SELL
-200 to -31
SELL ALL
< -200
BUY and STRONG BUY are downgraded to NEUTRAL when annualized expected return is below the 3.6% cash hurdle or when the lower return spread (return minus MAD) is below -1%. Sell-side visibility is protected: if the full-horizon annualized economic return is below -0.5%, the final rating is forced to at least PARTIALLY SELL even when volatility or shock dampening would otherwise soften the score toward NEUTRAL.
Formula Version is stored with the leaderboard output so every rank can be traced back to the exact scoring specification used at generation time. Different versions can change thresholds, weighting, volatility handling, dividend treatment, or diagnostic logic. Production leaderboard ranking uses the canonical dividend-inclusive score and signal fields.
About iPulse
Want the company and founder story behind the methodology?
The full Future Edge Group background, founder profile, strategic advisor details, public profiles, and leadership contact links now live on the dedicated About Us page.