IntrinsicTime/README.md

# IntrinsicTime

The directional-change operator, the intrinsic-time scaling laws, and the Alpha
Engine coastline trader, implemented in MQL5.

Companion code for the MQL5 article: https://www.mql5.com/en/articles/23814

## What it does

Physical time is an arbitrary clock for a market. Intrinsic time replaces it:
the clock only ticks when price reverses by a fixed threshold, so quiet periods
compress and active ones stretch. `DcOperator.mqh` is that clock: an online
operator that dissects a tick stream into directional-change and overshoot
legs, one price at a time, with no look-ahead.

That reframing comes with published scaling laws, which are empirical
regularities relating threshold size to event counts and move lengths.
`DcOS_ScalingLaws.mq5` measures them on your own broker's ticks and fits the
same laws on a Gaussian random walk as a control. On 17.8 million EUR/USD ticks
the market's exponents match the paper's closely (count law -1.944 against the
paper's -1.908, coastline -0.978 against -0.940). The control is the important
part. A walk sized to the market, one step per price change with the market's
own step volatility, reproduces the same exponents (-1.974 and -0.969). At these
scales the laws hold for pure noise too, so they do not by themselves reveal
market structure. A walk built with the paper's much coarser fixed step gives
shallower slopes and a false-looking gap, which is why the script's defaults
size the walk to EUR/USD.

The Alpha Engine is the trading side: a counter-trend coastline trader that adds
to a position at each adverse intrinsic event and trims each add at a profit of
delta, with inventory-skewed thresholds and a liquidity indicator around it.
`Ae_RandomWalk.mq5` runs the reference engine on 200 seeded random walks. Its
realised profit is positive on all 200, but its total profit, with the open
cascade marked at the last price, averages zero (+0.22, standard error 0.36):
the method turns noise into many small closed wins and a few large open losses,
not into an edge.

`AlphaEngine.mq5` is the Expert Advisor, eight limit-order agents on a hedging
account. On EUR/USD from 2 February to 31 July 2026 at the default inputs it
returned 10.68% with a 10.10% equity drawdown. That is +1,934 from take-profits,
-824 when the tester closed 24 leftover positions at the end of the run, and
-42 commission, with swap (-620 in total) already inside the first two figures.
The live agent has no whole-position exit, so a cascade that never recovers is
only halted, not closed.

## Layout

```
Include/IntrinsicTime/DcTypes.mqh            directional-change enums and event record
Include/IntrinsicTime/DcOperator.mqh         CDcOS, the directional-change/overshoot operator
Include/IntrinsicTime/ScalingLaws.mqh        operator bank, log-log fit, random-walk control
Include/IntrinsicTime/AeTypes.mqh            tick structure and intrinsic-event codes
Include/IntrinsicTime/AeRunner.mqh           CAeRunner, the log-threshold event runner
Include/IntrinsicTime/AeLiquidity.mqh        CAeLocalLiquidity, the liquidity indicator L
Include/IntrinsicTime/AeLimitOrder.mqh       reference limit order with de-cascade accounting
Include/IntrinsicTime/AeCoastlineTrader.mqh  the offline reference coastline trader
Include/IntrinsicTime/AeAlphaEngine.mqh      the eight-agent ensemble, offline reference form
Include/IntrinsicTime/AeLiveTrader.mqh       CAeLiveTrader, one live agent on real limit orders
Experts/IntrinsicTime/AlphaEngine.mq5        the Expert Advisor (hedging account required)
Scripts/IntrinsicTime/DcOS_SelfTest.mq5      known-answer tests for the operator
Scripts/IntrinsicTime/DcOS_ScalingLaws.mq5   scaling laws on live ticks against the control
Scripts/IntrinsicTime/Ae_SelfTest.mq5        known-answer tests for the trading model
Scripts/IntrinsicTime/Ae_RandomWalk.mq5      realised and total profit on random walks
```

Run the two self-tests first, then the scaling-law script on a fully synced
EUR/USD chart. For another symbol, set the walk's length, starting level and
per-step sigma to that symbol's own tick data before comparing exponents.

## Disclaimer

Educational code. The random-walk results show that the engine's closed-trade
profit is not evidence of an edge, and the backtest covers one pair over one
six-month window, so it demonstrates a working program rather than a strategy
with proven expectancy. Past behaviour of any model or dataset says nothing
about future results. Test on your own data and broker conditions before
drawing conclusions.