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Alpha Researcher agent + symbolic alpha DSL

Self-evolving LLM-driven factor mining wired into AlphaSwarm's deployment-consistent execution loop.

The loop​

Symbolic DSL vocabulary​

The full operator + field whitelist lives in alphaswarm/data/expressions_dsl.py.

Fields: $open, $high, $low, $close, $volume, $vwap, $returns.

Operators (curated): Ref, Delay, Mean, Std, Var, Skew, Kurt, Sum, Min, Max, Med, Mad, Quantile, Count, IdxMax, IdxMin, EMA, WMA, Slope, Rsquare, Resi, Corr, Cov, Greater, Less, Gt, Ge, Lt, Le, Eq, Ne, And, Or, Not, Mask, If, Add, Sub, Mul, Div, Abs, Sign, Log, Rank, Clip.

Numeric literals: integers + floats + bools + None + short strings.

Anything else (imports, attribute access, subscripts, lambdas, comprehensions, walrus, await, yield) raises :class:SymbolicAlphaError at compile time.

Example proposal​

{
  "name": "ema_crossover_pct",
  "formula": "Sign(EMA($close, 12) - EMA($close, 26)) * Rank(Std($returns, 20))",
  "rationale": "Combines MACD-style cross with vol-rank to favour high-vol trends.",
  "expected_horizon_bars": 5,
  "expected_direction": "either"
}

Compile + evaluate​

from alphaswarm_agents.quant import AlphaResearcher

researcher = AlphaResearcher(agent_spec_name="alpha_researcher")
proposal = researcher.propose(inputs={"intent": "find a short-horizon mean-reversion factor"})
result = researcher.evaluate(proposal, bars=bars)
print(result.metrics, result.reward)

Engine-agnostic FactorNode​

The compiled FactorNode feeds:

  • Event-driven engine via .compute(bars) returning a pd.Series.
  • vbt-pro orders mode via .compute_panel(bars_panel) returning a wide DataFrame.
  • Backtrader (optional) via .as_backtrader_indicator() returning a dynamic bt.Indicator subclass.

Companion agent: StrategyExecutor​

The StrategyExecutor agent decides WHICH RL experiment to train / paper-trade / promote based on the RAG rl_trajectory_summaries corpus and the live broker state. Routes lifecycle actions through RLRuntime (rule 16).

See also​