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asset⌬ modulemoduleprimary: init.luau·originates fromworld 60d37757-1…

core

Trains the engineered added connections inside the native fly simulation with paired reward trials on 2015 data. Sixteen pairs of 24-day rollouts (768 actual native decisions) compare opposite perturbations of the same weights on identical historical dates and starting cash. Rewa…

by◐Deata·posted 29d ago
What it does

Native historical trading experiment

Trains the engineered added connections inside the native fly simulation with paired reward trials on 2015 data. Sixteen pairs of 24-day rollouts (768 actual native decisions) compare opposite perturbations of the same weights on identical historical dates and starting cash. Reward is 10000 × log(final equity / initial equity), including commissions, spread, slippage and dividends. No action or inactivity bonus, supervised action labels, forced order, or test-date feedback enters training.

The two returns move the 96 shared synapse parameters toward the better perturbation, with bounded updates. This is a small derivative-free reward-search experiment, not proof of predictive skill or a biologically validated learning rule. After an actual decision-neuron silencing check, weights freeze for the entire 2016–17 replay. Native motor spike counts alone select BUY/HOLD/SELL; the exchange may reject an infeasible order but cannot substitute a different action.

Starting cash is $1,000. Automatic two-second transitions carry cash, shares, costs, dividends, drawdown and comparison accounts across all five contiguous periods. The final dataset date stops playback and preserves the account; Restart replay explicitly starts over and is marked as repeated evidence.

A trading day takes 10/2.5/0.833 seconds at 1x/4x/12x. The decision window locks the action and its sampled rates; native activity continues between decisions without changing that locked action. Pause stops both. Random comparison accounts and a one-time 25% passive allocation use the same exchange assumptions.

Interface

What this asset declares: the schema it conforms to, what it exposes, and the rendered structured payload.

conforms to

zero/source-extract/v2

checksum(w: ?) → void

argtypedescription
w?

randomStats(accounts: ?, price: ?, equity: ?) → void

argtypedescription
accounts?
price?
equity?

new(S: ?) → void

argtypedescription
S?

startRewardTrial( ) → void

train( ) → void

beginSession(seed: ?, carry: ?) → void

argtypedescription
seed?
carry?

audit( ) → void

resultSummary( ) → void

closeSession( ) → void

update(dt: ?) → void

argtypedescription
dt?

destroy( ) → void

Sub-parts

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module · born here
❒asset
# Historical AAPL market Replays 506 downloaded daily OHLCV bars from 2015-02-17 to 2017-02-16. Training ends 2015-12-31; five nonoverlapping later windows contain 284 trading days. See ../data/README.md for provenance and execution assumptions. No synthetic price process is used. Random numbers are used only for baseline decisions. The sensor encoder receives past observed returns/volatility and current account state. A decision at a close can fill only at the next opening price. Modelled commission, adverse spread/slippage, limited position size and dividend entitlements apply equally to the fly and comparison accounts. Buying on an ex-date earns no dividend for that ex-date.
▲ 0↑ born
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module · born here
❒asset
# Reward-trained circuit in the native fly connectome Loads 164,587 native neurons and 25,563,197 biological synapses. The 32-channel observation stimulates 1,024 selected native sensory neurons. Ninety-six parameters are shared across 49,152 engineered added connections into 96 native decision neurons. Biological weights stay fixed; reward search changes the added connections. An adaptation of the installed flyOnlineLearning parameter-search approach tests positive and negative random weight perturbations on matched 2015 episodes. All 768 training actions come from actual GPU native spike counters. The objective is net portfolio log return after costs. Cash-only episodes earn zero, profitable episodes positive reward and losing episodes negative reward. Equal returns cause no parameter update. Both initial action drive and perturbation distributions are symmetric across actions; no BUY/HOLD/SELL target labels are fitted. Sixteen paired updates are a limited demonstration, not a strong optimization budget. Profitable training trials, active trading or a winning historical replay do not establish forecasting skill. Evaluation dates have been inspected during development and are no longer a pristine research holdout. Evaluation freezes added weights as well. A decision uses a locked native spike sample while the brain continues evolving for live telemetry. The cloud uses broker.neuralActivity: ongoing trace is compressed without clipping at 1, and actual current-step spikes flash white. Quiet neurons stay dim. Display samples at frame rate and cannot expose every millisecond spike.
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backing path · broker/core.module

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