Methodology
State-adaptive signals — not rigid indicators
Algo Bot is designed as a state-adaptive trading system: a Hidden Markov Model infers the market regime; a Markov Decision Process decides what to do. Patterns and technicals feed the model; rated signals are what you see.
01
Market observations
Candles and derived features — log returns, volatility, RSI, ATR, volume, and pattern tags — become the observable inputs for the model.
02
HMM: infer the regime
A Hidden Markov Model estimates the hidden market state (e.g. bullish, bearish, sideways) as a probabilistic belief — not a single fragile indicator.
03
MDP: choose the action
That belief feeds a Markov Decision Process that picks buy, sell, or hold by optimizing a risk-adjusted reward (e.g. Sharpe-style), instead of fixed RSI/MA rules.
04
Publish rated signals
Only stronger decisions become product signals — side, levels, and a success rating — then delivery to Telegram, Discord, and the public ledger.
What is what
HMM vs MDP in plain language
These are the two research building blocks behind the product. One reads the market’s hidden state; the other chooses an action under that state.
Hidden Markov Model
HMM
Markets have states we can’t directly see (regime). An HMM treats price/volatility features as “emissions” from those hidden states, then estimates which regime we’re likely in right now.
Regime probability
Belief state
Instead of “the market is bullish / done,” we keep a probability mix across regimes. That belief updates as new candles arrive.
Markov Decision Process
MDP
Once we have a state, we need an action. An MDP maps state (+ position context) to buy / sell / hold by maximizing expected risk-adjusted reward — the control policy.
Product confidence
Success rating
Product-facing score for how publishable a setup looks: regime clarity, policy confidence, and expected quality vs noisy firehose alerts.
Why pair them?
An HMM alone describes regimes — it doesn’t say what to trade. An MDP alone needs a useful state — otherwise it optimizes noise. Together they form a state-adaptive loop: adapt when crypto regimes shift, then act with a policy aimed at risk-adjusted quality, not every pattern that appears on a chart.
Research → product
How the thesis becomes a signal
Same pipeline, two audiences: thesis language for the model; product language for Telegram, Discord, and the ledger.
Emissions (returns, vol, RSI…)
Pattern & technical inputs you recognize on charts
HMM regimes + belief
Market regime context behind each signal
MDP action (buy / sell / hold)
Signal direction — or no publish if hold wins
Risk-adjusted reward
Why we prefer quality over raw win-rate
Walk-forward evaluation
Public ledger outcomes you can audit
Primary research pair is BTC/USDT, with walk-forward evaluation against buy-and-hold and simple rule-based benchmarks. We do not custody your funds — v1 is signals you can execute yourself.
Free vs Paid
What we show publicly
Transparency is the product. Actionable timing is the upgrade.
Free receives direction, regime/pattern label, success rating, and the performance ledger — enough to judge whether the system is honest before paying.
Paid ($14.99/mo) receives the full signal at publication: entry, stop, take-profits, and instant delivery on Telegram and Discord.
We do not delete losing signals from the public history. Expired and stopped trades remain visible alongside winners.
See the ledger yourself
Review sample performance, then request an invitation for live channel access.