Kivendove AI processes high-frequency market data, order-flow patterns and sentiment shifts, then converts the output of stochastic models into signals you can act on — without requiring you to interpret the underlying maths yourself.
Kivendove AI was built on the premise that a trading signal is only as trustworthy as the process behind it. Rather than presenting a black box, the platform exposes the logic of every model: which data it consumed, how confidence was scored, and how it performed under prior market stress.
The team behind Kivendove AI works at the intersection of applied statistics and market microstructure, focusing specifically on the liquidity and volatility patterns of UK-listed instruments alongside major FX and index pairs.
Each component below addresses a specific point of failure in manual trading: incomplete data, delayed reaction, and emotional override.
Kivendove AI applies stochastic modelling to price and volume series, generating a probability distribution of likely outcomes rather than a single forecast. Signals are only issued once a model's confidence score clears a threshold you set, which means fewer trades but a higher proportion of statistically supported ones. In practice, this reduces the frequency of low-conviction entries that erode returns through fees and slippage.
The platform continuously scans regulatory filings, wire services and market commentary, quantifying sentiment change rather than reacting to headlines in isolation. When sentiment diverges from price action, that divergence is flagged as a risk factor and factored into position sizing. This narrows the gap between a public announcement and a considered response, without requiring you to monitor news feeds manually.
Discretionary trading is vulnerable to loss aversion and overconfidence, particularly after a string of wins or losses. Kivendove AI enforces the risk parameters you define — position limits, stop distances, exposure caps — consistently, regardless of recent outcomes. The model does not adjust its discipline based on how the last trade felt, which is precisely the point.
We do not use testimonials as evidence of performance. Instead, every strategy is evaluated against the metrics below, tracked separately across bull, bear and sideways conditions rather than blended into a single headline figure.
Return earned per unit of volatility absorbed, recalculated after every backtest cycle.
The largest peak-to-trough decline recorded, tracked separately by regime to expose regime-specific risk.
The proportion of signals closing in profit, segmented rather than averaged across market conditions.
Time elapsed between data ingestion and signal delivery, measured under live market load.
Rather than reporting a single blended return, each strategy's backtest output is broken down by the market regime in which it was generated. This makes it possible to see whether a strategy's apparent strength comes from broad conditions or from a narrow, favourable window.
Methodology note: all backtests use walk-forward validation on out-of-sample data, incorporate historical UK equity and FX transaction costs and slippage estimates, and exclude any period used to train the underlying model. Past performance in testing does not guarantee future results.
Onboarding is designed to take hours, not weeks, while still giving you control over how the model behaves.
Link your existing brokerage account through a secure, read-first API connection. Historical trade and price data is imported so the model has context before it issues a single signal.
Set your own risk parameters: maximum position size, stop-loss distance, exposure per sector, and the timeframes you trade. The model adapts its signal frequency and confidence threshold to fit these constraints.
Signals are delivered with a confidence score and suggested position size. You can route execution directly through your connected broker or review each signal manually before acting.
The underlying model does not change; the timeframe, confidence threshold and risk parameters it operates under do.
For minute-level trading, Kivendove AI tightens its confidence threshold and shortens its data window, focusing on order-flow imbalances and short-term liquidity gaps common in LSE mid-cap names during the market open and close. Signals are timestamped to the second so latency can be measured against your own execution speed.
Typical horizon: seconds to minutes
For multi-day holds, the model widens its data window to include sentiment shifts ahead of scheduled events such as earnings releases and Bank of England announcements, weighing them against historical FTSE 100 volatility patterns. Position sizing adjusts automatically as confidence builds or decays over the holding period.
Typical horizon: two to ten trading days
For longer-term allocation decisions, Kivendove AI shifts focus from short-term price signals to macro and sector-level drift, flagging when a portfolio's actual weighting has diverged from its target allocation. Recommendations are issued on a quarterly cadence rather than daily, in line with the lower turnover this style requires.
Typical horizon: quarterly review cycle
All data in transit is encrypted using TLS 1.2 or higher, and data at rest is encrypted using AES-256. Brokerage connections use read-first, revocable API tokens rather than stored login credentials, so access can be withdrawn at any time from your broker's side without contacting us.
Signal latency depends on the strategy timeframe: scalping-oriented models are optimised for sub-second delivery, while swing and rebalancing models operate on longer, less latency-sensitive cycles. Latency figures for your specific setup are shown in your account dashboard rather than quoted as a single fixed number, since network conditions vary.
Pricing is structured in tiers based on the number of connected strategies and data refresh frequency, with full details provided before you connect a live brokerage account. There are no performance-based fees; the cost of the platform does not change based on how your trades perform.
Onboarding is currently staged in cohorts, so that model performance and support quality remain consistent as new accounts are connected.
No obligation. You will be contacted before any brokerage connection is required.