01 · Overview
Agent_007 is a research and paper-trading system for crypto markets, built around a single idea: a trading decision should never rest on one signal, one model, or one person's conviction. Instead, several independent layers — market context, strategy validation, portfolio state, risk limits, learned evidence, and execution — each reach their own judgment on a candidate trade. A position only opens when all of them agree.
The system currently operates in paper-trading mode only. No real capital is at risk, and live order execution is disabled at the code level, not just by configuration — a distinction explained in Section 6.
02 · The Problem
Most retail trading automation fails for a boring reason: it is a collection of disconnected signals bolted together, not a coordinated system. A moving-average crossover fires an entry. A separate, unrelated script decides position size. Nothing checks whether the new position is just a leveraged bet on something the account already holds. Nothing tracks whether the strategy has actually made money historically, or is simply the last one someone happened to backtest.
Agent_007's architecture exists specifically to close those gaps: every entry candidate passes through the same sequence of independent checks, every time, with no shortcuts.
03 · System Architecture
Six layers, each with a narrow, well-defined responsibility:
Market Intelligence
Technical structure (trend, momentum, volatility) combined with derivatives context — funding rates and open-interest shifts — and multi-timeframe alignment across daily, 4-hour, and 1-hour charts before a signal is even considered.
Strategy Intelligence
Five independent, rule-based strategy families (trend-following, breakout, mean-reversion, volatility expansion, momentum continuation), each with its own parameters — never one permanent rule applied everywhere.
Portfolio Intelligence
Position sizing that accounts for confidence, volatility, current portfolio heat, cash reserves, and — critically — correlation against every position already open, so the system can't silently concentrate risk into one correlated bet.
Risk Intelligence
Hard exposure, concentration, and drawdown limits that can block a decision outright, independent of how confident the signal-generation layer is.
Research Engine
Historical backtesting, walk-forward validation across rolling time windows, and Monte Carlo stress testing turn a strategy idea into measurable, falsifiable evidence before it is ever allowed to trade — even on paper.
Execution Layer
Only a candidate that has cleared every prior layer reaches order routing, and every fill, stop, and exit is logged with the full reasoning that justified it.
A seventh capability, self-learning, sits alongside these layers rather than above them: once a strategy/symbol pair has accumulated real closed paper trades, its own historical win rate and expectancy are allowed to make a conservative, bounded adjustment (±10–25%) to future position sizing — never enough to override the other six layers, and never anything until real evidence exists to learn from.
What separates this from most trading agents is not any single layer — it is that a candidate must clear all of them, in the same order, every time, with no path that skips the evidence requirement described in Section 5. Most automated trading tools go directly from a signal to an order. Agent_007 cannot: the evidence gate is enforced in code, not configuration, so there is no setting that bypasses it.
04 · Market Universe & Data
Rather than trading a fixed, manually-curated list of coins, Agent_007 continuously discovers a broad universe of liquid markets — filtered by market capitalization, trading volume, and volume-to-cap ratio — and automatically excludes stablecoins and wrapped assets. Coins that fall out of the liquidity bar are retired from active scanning but never silently dropped: any open position on a retired coin remains fully priced and monitored until it closes.
Historical and live data — 4-hour and 1-hour price candles, funding rates, and open-interest — is refreshed automatically on a schedule, with each source designed to fail independently: a data gap on one coin or one derivatives feed never halts collection for the rest of the universe.
05 · Validation & Evidence Standard
A strategy does not reach paper trading because it looks promising. It has to survive:
- Walk-forward validation across multiple rolling out-of-sample windows, not a single historical backtest that is easy to overfit.
- Minimum sample-size and bootstrap-confidence gates — a strategy with too few historical trades to be statistically meaningful is explicitly held at "evidence candidate" status, not promoted.
- Monte Carlo stress testing of the resulting trade distribution, to characterize drawdown and ruin probability rather than assume the historical path was the only possible one.
- Cross-asset and parameter-neighborhood robustness checks, so a result that only works for one exact parameter combination on one exact coin is treated with appropriate skepticism.
Every gate's pass/fail state is stored, not just the final verdict — so any promotion decision can be traced back to exactly which evidence justified it.
06 · Risk & Safety Governance
Live execution is not a setting that can be toggled on. The paper-trading execution path and the (currently unused) live-execution path are architecturally separate, and the system's own readiness service will only ever report a strategy as "live-eligible" — it never enables live trading itself. Turning on real-money execution requires a separate, explicit, human deployment decision outside the system entirely.
Before that decision could ever reasonably be made, the system requires: at least 100 closed paper trades per strategy, a positive expectancy with profit factor above 1.5, maximum drawdown under 10%, and a positive lower-bound bootstrap confidence interval — among other gates. As of this writing, every strategy in the system is still in evidence collection, and zero have cleared that bar.
07 · Current Status & Roadmap
Data infrastructure, strategy research, historical validation and evidence scoring.
Continuous, unattended decision cycles under full portfolio and risk constraints.
Correlation-aware capital weighting and portfolio governance across multiple live strategies at once.
Real capital, only after every statistical, operational, and safety gate is independently satisfied.
08 · Disclaimer
Agent_007 is a research and software project. It is not financial, investment, or trading advice, and nothing in this document or on this website should be interpreted as a recommendation to buy, sell, or hold any asset. The system currently executes trades in a simulated, paper-only environment with no real capital at risk. Historical backtests, walk-forward results, and paper-trading performance — whether past or future — are not a guarantee or reliable indicator of future results, simulated or real. Cryptocurrency markets are highly volatile, and any future transition to live, real-capital execution would carry a genuine risk of financial loss. Nothing described here constitutes an offer or solicitation of any kind.