Model

Quant developer: 20 ways to use Ora

Wires exact rules and forecasts into pipelines, tests and order systems. Each example is a complete, runnable request with the real answers it returned (32 requests, captured 2026-10-31), what they mean and, where it fits, the action it would send.

Run withAnswers were captured on ora-fin-0.1.1. Another model changes the request JSON and the Playground link; run it there to see its answers.

Rule functions and facts used by this player (64)

sma 8 ref 7 offset 6 avg 5 ema 3 abs 3 atr 3 crossup 2 wma 2 adx 2 highest 2 log 2 sqrt 2 std 2 zscore 2 rsi 2 sum 2 mean 2 crossdown 1 diminus 1 diplus 1 linreg 1 median 1 gap_down 1 gap_up 1 inside_bar 1 marubozu 1 outside_bar 1 spinning_top 1 cum 1 rec 1 rma 1 macd 1 macdsignal 1 cci 1 stochd 1 stochk 1 willr 1 days_since_earnings 1 days_to_earnings 1 last_earnings_move 1 next_report_amc 1 barssince 1 highestbars 1 lowestbars 1 bblower 1 between 1 iif 1 sign 1 greatest 1 hammer 1 hanging_man 1 inverted_hammer 1 least 1 hl2 1 hlc3 1 ohlc4 1 tr 1 swinghigh 1 swinglow 1 lowest 1 max 1 obv 1 percentrank 1

Examples 11–20 of 20 · page 2 of 2

Quant developer · example 11

Volatility-targeting inputs for TLT position sizing

Get realized-volatility states and the forward volatility forecast for a bond ETF before resizing a vol-targeted sleeve.

ora-fin-0.1.1TLT5 questions1 action

StateTLT
Questions5 asked
Answersreal, captured
Actions1 rule · dry run

State

{"symbol":"TLT"} The server fills in the latest completed session.

Questions

`hv`	rule	`std(log(C / C[-1]), 20) * sqrt(252) > 0.15` 20-day realized volatility above 15% a year
`atr_up`	rule	`atr(14) > 1.2 * ref(atr(14), -10)` ATR up 20% in two weeks
`vol5`	English → rule	Is 5-day volatility higher than 20-day volatility?
`vol_up`	forecast	Will volatility be higher over the next month? Higher volatility next month
`hv30`	forecast	Will annualized volatility over the next 21 sessions exceed 30%? Next-month volatility above 30%

Answers

Question	Answer	Reading
`hv` 20-day realized volatility above 15% a year	0.0537	false
`atr_up` ATR up 20% in two weeks	0.0537	false
`vol5` Is 5-day volatility higher than 20-day volatility compiled to `std(log(C / C[-1]), 5) * sqrt(5 / 4) > std(log(C / C[-1]), 20) * sqrt(20 / 19)`	0.2703	false
`vol_up` Higher volatility next month	0.583	58% · close to even
`hv30` Next-month volatility above 30%	0.497	50% · close to even

Actions

Rules on the answers decide what to send.

vol_up >= 0.6 or hv >= 0.6→Slackquant-dev

What each action would send 0 of 1 fired · dry run, nothing sent

resizerule false: skipped

What it means

For TLT on the session of 2026-10-30: Not true: 20-day realized volatility above 15% a year; ATR up 20% in two weeks; Is 5-day volatility higher than 20-day volatility. Forecasts: Higher volatility next month 58%; Next-month volatility above 30% 50%. No action fired: every rule was false.

Sizing code reads the realized rule for today and the forecast for the month; the Slack note tells the developer on call that a resize will run.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "TLT"
  },
  "questions": {
    "hv": {
      "type": "yes_no",
      "ask": "std(log(C / C[-1]), 20) * sqrt(252) > 0.15"
    },
    "atr_up": {
      "type": "yes_no",
      "ask": "atr(14) > 1.2 * ref(atr(14), -10)"
    },
    "vol5": {
      "type": "yes_no",
      "instructions": "Is 5-day volatility higher than 20-day volatility?"
    },
    "vol_up": {
      "type": "yes_no",
      "instructions": "Will volatility be higher over the next month?"
    },
    "hv30": {
      "type": "yes_no",
      "instructions": "Will annualized volatility over the next 21 sessions exceed 30%?"
    }
  },
  "actions": [
    {
      "id": "resize",
      "when": "vol_up >= 0.6 or hv >= 0.6",
      "do": "slack",
      "connection": "quant-dev",
      "message": "{{symbol}} ({{as_of}}): vol-target sleeve needs a resize check. Realized vol above 15% {{hv|pct}}, higher vol next month {{vol_up|pct}}."
    }
  ],
  "dry_run": true
}

Response JSON (TLT, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "hv": {
      "yes_no": 0.0537,
      "rule": "std(log(C / C[-1]), 20) * sqrt(252) > 0.15",
      "basis": "rule"
    },
    "atr_up": {
      "yes_no": 0.0537,
      "rule": "atr(14) > 1.2 * ref(atr(14), -10)",
      "basis": "rule"
    },
    "vol5": {
      "yes_no": 0.2703,
      "rule": "std(log(C / C[-1]), 5) * sqrt(5 / 4) > std(log(C / C[-1]), 20) * sqrt(20 / 19)",
      "basis": "pattern"
    },
    "vol_up": {
      "yes_no": 0.583
    },
    "hv30": {
      "yes_no": 0.497
    }
  },
  "context": {
    "symbol": "TLT",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "resize",
      "fired": false,
      "status": "skipped"
    }
  ]
}

atr log offset ref sqrt std

Try it in the Playground

Quant developer · example 12

Mean-reversion candidates across consumer staples

Find staples trading below their lower band or with a negative z-score, inside a long uptrend.

ora-fin-0.1.15 symbols, one request each5 questions

State5 symbols
Questions5 asked
Answersreal, captured
Actionsnone

State

{"symbol":"PG"} then KO, PEP, CL, KMB, one request each. The server fills in the latest completed session.

Questions

`band`	rule	`C < bblower(C, 20, 2)` Closed under the lower Bollinger band
`zs`	rule	`zscore(C, 20) < -2` Two deviations under the 20-day mean
`trend`	English → rule	Is the stock above its 200-day moving average?
`back_inside`	rule	`crossup(C, bblower(C, 20, 2))` Crossed back above the lower band
`up_week`	forecast	Will the stock be higher in a week? Higher in a week

Answers

Symbol	`band`rule	`zs`rule	`trend`rule	`back_inside`rule	`up_week`forecast
`PG`	0.0573	0.0586	0.9431	0.05	0.485
`KO`	0.0568	0.0585	0.9463	0.05	0.549
`PEP`	0.0537	0.0585	0.1034	0.05	0.505
`CL`	0.0601	0.0629	0.9337	0.05	0.513
`KMB`	0.0588	0.0712	0.8926	0.05	0.528

true false not known for rules; forecasts are odds. Hover a column for its question.

What it means

Session of 2026-10-30, 5 symbols: Closed under the lower Bollinger band: true for none of 5. Two deviations under the 20-day mean: true for none of 5. Is the stock above its 200-day moving average: true for PG, KO, CL, KMB of 5. Crossed back above the lower band: true for none of 5. Highest odds for “Higher in a week”: KO at 55%.

The batch returns the same keys for every name; a candidate needs a band or z-score rule true while the long trend holds.

Request JSON (PG; the others differ only in the symbol)

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "PG"
  },
  "questions": {
    "band": {
      "type": "yes_no",
      "ask": "C < bblower(C, 20, 2)"
    },
    "zs": {
      "type": "yes_no",
      "ask": "zscore(C, 20) < -2"
    },
    "trend": {
      "type": "yes_no",
      "instructions": "Is the stock above its 200-day moving average?"
    },
    "back_inside": {
      "type": "yes_no",
      "ask": "crossup(C, bblower(C, 20, 2))"
    },
    "up_week": {
      "type": "yes_no",
      "instructions": "Will the stock be higher in a week?"
    }
  }
}

Response JSON (PG, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "band": {
      "yes_no": 0.0573,
      "rule": "C < bblower(C, 20, 2)",
      "basis": "rule"
    },
    "zs": {
      "yes_no": 0.0586,
      "rule": "zscore(C, 20) < -2",
      "basis": "rule"
    },
    "trend": {
      "yes_no": 0.9431,
      "rule": "C > sma(C, 200)",
      "basis": "comparison"
    },
    "back_inside": {
      "yes_no": 0.05,
      "rule": "crossup(C, bblower(C, 20, 2))",
      "basis": "rule"
    },
    "up_week": {
      "yes_no": 0.485
    }
  },
  "context": {
    "symbol": "PG",
    "as_of": "2026-10-30"
  }
}

bblower crossup sma zscore

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Quant developer · example 13

Adaptive RSI thresholds for JPM with iif and between

Use a regime-dependent oversold line and a neutral band check instead of hard-coding one threshold.

ora-fin-0.1.1JPM4 questions

StateJPM
Questions4 asked
Answersreal, captured
Actionsnone

State

{"symbol":"JPM"} The server fills in the latest completed session.

Questions

`adaptive`	rule	`rsi(C, 14) < iif(C > sma(C, 200), 40, 30)` RSI under 40 in an uptrend, under 30 otherwise
`neutral`	rule	`between(rsi(C, 14), 40, 60)` RSI between 40 and 60
`sign_run`	rule	`sum(sign(C - C[-1]), 10) > 2` net up days over the last 10 sessions above 2
`up_week`	forecast	Will the stock be higher in a week? Higher in a week

Answers

Question	Answer	Reading
`adaptive` RSI under 40 in an uptrend, under 30 otherwise	0.1428	false
`neutral` RSI between 40 and 60	0.95	true
`sign_run` net up days over the last 10 sessions above 2	0.45	on the line (false)
`up_week` Higher in a week	0.558	56% · close to even

What it means

For JPM on the session of 2026-10-30: True now: RSI between 40 and 60. Not true: RSI under 40 in an uptrend, under 30 otherwise. On the line: net up days over the last 10 sessions above 2. Forecasts: Higher in a week 56%.

The adaptive rule encodes the regime once in the rule text, so the pipeline does not need its own branching.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "JPM"
  },
  "questions": {
    "adaptive": {
      "type": "yes_no",
      "ask": "rsi(C, 14) < iif(C > sma(C, 200), 40, 30)"
    },
    "neutral": {
      "type": "yes_no",
      "ask": "between(rsi(C, 14), 40, 60)"
    },
    "sign_run": {
      "type": "yes_no",
      "ask": "sum(sign(C - C[-1]), 10) > 2"
    },
    "up_week": {
      "type": "yes_no",
      "instructions": "Will the stock be higher in a week?"
    }
  }
}

Response JSON (JPM, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "adaptive": {
      "yes_no": 0.1428,
      "rule": "rsi(C, 14) < iif(C > sma(C, 200), 40, 30)",
      "basis": "rule"
    },
    "neutral": {
      "yes_no": 0.95,
      "rule": "between(rsi(C, 14), 40, 60)",
      "basis": "rule"
    },
    "sign_run": {
      "yes_no": 0.45,
      "rule": "sum(sign(C - C[-1]), 10) > 2",
      "basis": "rule"
    },
    "up_week": {
      "yes_no": 0.558
    }
  },
  "context": {
    "symbol": "JPM",
    "as_of": "2026-10-30"
  }
}

between iif offset rsi sign sma sum

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Quant developer · example 14

Wick geometry on XOM with least and greatest

Compute long-lower-wick and long-upper-wick conditions directly, then compare with the named candle patterns.

ora-fin-0.1.1XOM5 questions

StateXOM
Questions5 asked
Answersreal, captured
Actionsnone

State

{"symbol":"XOM"} The server fills in the latest completed session.

Questions

`long_lower`	rule	`least(O, C) - L > 2 * abs(C - O)` lower wick more than twice the body
`long_upper`	rule	`H - greatest(O, C) > 2 * abs(C - O)` upper wick more than twice the body
`hammer`	rule	`hammer()` hammer candle: Lower wick at least twice the body, upper wick at most 10% of the range, closing below the close five bars back.
`hanging`	rule	`hanging_man()` hanging man candle: Hammer shape, closing above the close five bars back.
`inv_hammer`	rule	`inverted_hammer()` inverted hammer candle: Shooting-star shape after a five-bar decline.

Answers

Question	Answer	Reading
`long_lower` lower wick more than twice the body	0.2621	false
`long_upper` upper wick more than twice the body	0.2569	false
`hammer` hammer candle: Lower wick at least twice the body, upper wick at most 10% of the range, closing below the close five bars back.	0.0971	false
`hanging` hanging man candle: Hammer shape, closing above the close five bars back.	0.0971	false
`inv_hammer` inverted hammer candle: Shooting-star shape after a five-bar decline.	0.1089	false

What it means

For XOM on the session of 2026-10-30: Not true: lower wick more than twice the body; upper wick more than twice the body; hammer candle: Lower wick at least twice the body, upper wick at most 10% of the range, closing below the close five bars back.; hanging man candle: Hammer shape, closing above the close five bars back.; inverted hammer candle: Shooting-star shape after a five-bar decline..

The raw wick rules are shape only; the named patterns add the prior-trend condition, which explains when the two disagree.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "XOM"
  },
  "questions": {
    "long_lower": {
      "type": "yes_no",
      "ask": "least(O, C) - L > 2 * abs(C - O)"
    },
    "long_upper": {
      "type": "yes_no",
      "ask": "H - greatest(O, C) > 2 * abs(C - O)"
    },
    "hammer": {
      "type": "yes_no",
      "ask": "hammer()"
    },
    "hanging": {
      "type": "yes_no",
      "ask": "hanging_man()"
    },
    "inv_hammer": {
      "type": "yes_no",
      "ask": "inverted_hammer()"
    }
  }
}

Response JSON (XOM, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "long_lower": {
      "yes_no": 0.2621,
      "rule": "least(O, C) - L > 2 * abs(C - O)",
      "basis": "rule"
    },
    "long_upper": {
      "yes_no": 0.2569,
      "rule": "H - greatest(O, C) > 2 * abs(C - O)",
      "basis": "rule"
    },
    "hammer": {
      "yes_no": 0.0971,
      "rule": "hammer()",
      "basis": "rule"
    },
    "hanging": {
      "yes_no": 0.0971,
      "rule": "hanging_man()",
      "basis": "rule"
    },
    "inv_hammer": {
      "yes_no": 0.1089,
      "rule": "inverted_hammer()",
      "basis": "rule"
    }
  },
  "context": {
    "symbol": "XOM",
    "as_of": "2026-10-30"
  }
}

abs greatest hammer hanging_man inverted_hammer least

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Quant developer · example 15

Typical-price and midpoint checks on IWM

Validate derived price series (hl2, hlc3, ohlc4, true range) used in a feature store.

ora-fin-0.1.1IWM5 questions

StateIWM
Questions5 asked
Answersreal, captured
Actionsnone

State

{"symbol":"IWM"} The server fills in the latest completed session.

Questions

`upper_half`	rule	`C > hl2()` closed in the upper half of the day's range
`typical`	rule	`hlc3() > sma(hlc3(), 20)` typical price above its 20-day average
`ohlc`	rule	`ohlc4() > sma(C, 10)` average of the four prices above the 10-day average close
`wide`	rule	`tr() > 1.5 * ref(atr(14), -1)` True range 1.5× yesterday's ATR
`mean100`	rule	`C > mean(C, 100)` close above its 100-day mean

Answers

Question	Answer	Reading
`upper_half` closed in the upper half of the day's range	0.2555	false
`typical` typical price above its 20-day average	0.7406	true
`ohlc` average of the four prices above the 10-day average close	0.8215	true
`wide` True range 1.5× yesterday's ATR	0.8915	true
`mean100` close above its 100-day mean	0.1618	false

What it means

For IWM on the session of 2026-10-30: True now: typical price above its 20-day average; average of the four prices above the 10-day average close; True range 1.5× yesterday's ATR. Not true: closed in the upper half of the day's range; close above its 100-day mean.

These are deterministic checks; any mismatch with the feature store is a bug in one of the two, never a model judgment.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "IWM"
  },
  "questions": {
    "upper_half": {
      "type": "yes_no",
      "ask": "C > hl2()"
    },
    "typical": {
      "type": "yes_no",
      "ask": "hlc3() > sma(hlc3(), 20)"
    },
    "ohlc": {
      "type": "yes_no",
      "ask": "ohlc4() > sma(C, 10)"
    },
    "wide": {
      "type": "yes_no",
      "ask": "tr() > 1.5 * ref(atr(14), -1)"
    },
    "mean100": {
      "type": "yes_no",
      "ask": "C > mean(C, 100)"
    }
  }
}

Response JSON (IWM, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "upper_half": {
      "yes_no": 0.2555,
      "rule": "C > hl2()",
      "basis": "rule"
    },
    "typical": {
      "yes_no": 0.7406,
      "rule": "hlc3() > sma(hlc3(), 20)",
      "basis": "rule"
    },
    "ohlc": {
      "yes_no": 0.8215,
      "rule": "ohlc4() > sma(C, 10)",
      "basis": "rule"
    },
    "wide": {
      "yes_no": 0.8915,
      "rule": "tr() > 1.5 * ref(atr(14), -1)",
      "basis": "rule"
    },
    "mean100": {
      "yes_no": 0.1618,
      "rule": "C > mean(C, 100)",
      "basis": "rule"
    }
  },
  "context": {
    "symbol": "IWM",
    "as_of": "2026-10-30"
  }
}

atr hl2 hlc3 mean ohlc4 ref sma tr

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Quant developer · example 16

Swing-structure features for CVX with swinghigh and swinglow

Encode the last two swing highs and lows as structure features for a trend-following model.

ora-fin-0.1.1CVX5 questions2 actions

StateCVX
Questions5 asked
Answersreal, captured
Actions2 rules · dry run

State

{"symbol":"CVX"} The server fills in the latest completed session.

Questions

`hh`	rule	`swinghigh(3, 1) > swinghigh(3, 2)` latest 3-bar swing high above the one before
`hl`	rule	`swinglow(3) > swinglow(3, 2)` Higher swing lows
`above_sh`	rule	`C > swinghigh(5)` Above its last confirmed swing high
`near_sup`	rule	`C >= swinglow(5) and C <= 1.03 * swinglow(5)` Within 3% above the last swing low
`break_res`	forecast	Will it break above resistance this week? Breaking resistance this week

Answers

Question	Answer	Reading
`hh` latest 3-bar swing high above the one before	0.9416	true
`hl` Higher swing lows	0.4316	on the line (false)
`above_sh` Above its last confirmed swing high	0.9333	true
`near_sup` Within 3% above the last swing low	0.0537	false
`break_res` Breaking resistance this week	0.503	50% · close to even

Actions

Rules on the answers decide what to send.

hh >= 0.6 and hl >= 0.6→Webhookfeature-store

else→Webhookfeature-store

What each action would send 1 of 2 fired · dry run, nothing sent

structurerule false: skipped

no_structurewould send (dry run)Webhook → feature-store

{
  "entity": "CVX",
  "as_of": "2026-10-30",
  "structure": "none"
}

What it means

For CVX on the session of 2026-10-30: True now: latest 3-bar swing high above the one before; Above its last confirmed swing high. Not true: Within 3% above the last swing low. On the line: Higher swing lows. Forecasts: Breaking resistance this week 50%. Fired: no_structure (Webhook → feature-store).

Swing pivots are confirmed bars later, so these features never look ahead; that is what makes them safe to train on.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "CVX"
  },
  "questions": {
    "hh": {
      "type": "yes_no",
      "ask": "swinghigh(3, 1) > swinghigh(3, 2)"
    },
    "hl": {
      "type": "yes_no",
      "ask": "swinglow(3) > swinglow(3, 2)"
    },
    "above_sh": {
      "type": "yes_no",
      "ask": "C > swinghigh(5)"
    },
    "near_sup": {
      "type": "yes_no",
      "ask": "C >= swinglow(5) and C <= 1.03 * swinglow(5)"
    },
    "break_res": {
      "type": "yes_no",
      "instructions": "Will it break above resistance this week?"
    }
  },
  "actions": [
    {
      "id": "structure",
      "when": "hh >= 0.6 and hl >= 0.6",
      "do": "webhook",
      "connection": "feature-store",
      "body": {
        "entity": "{{symbol}}",
        "as_of": "{{as_of}}",
        "structure": "uptrend",
        "p_break_res": "{{break_res}}"
      }
    },
    {
      "id": "no_structure",
      "when": "else",
      "do": "webhook",
      "connection": "feature-store",
      "body": {
        "entity": "{{symbol}}",
        "as_of": "{{as_of}}",
        "structure": "none"
      }
    }
  ],
  "dry_run": true
}

Response JSON (CVX, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "hh": {
      "yes_no": 0.9416,
      "rule": "swinghigh(3, 1) > swinghigh(3, 2)",
      "basis": "rule"
    },
    "hl": {
      "yes_no": 0.4316,
      "rule": "swinglow(3) > swinglow(3, 2)",
      "basis": "rule"
    },
    "above_sh": {
      "yes_no": 0.9333,
      "rule": "C > swinghigh(5)",
      "basis": "rule"
    },
    "near_sup": {
      "yes_no": 0.0537,
      "rule": "C >= swinglow(5) and C <= 1.03 * swinglow(5)",
      "basis": "rule"
    },
    "break_res": {
      "yes_no": 0.503
    }
  },
  "context": {
    "symbol": "CVX",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "structure",
      "fired": false,
      "status": "skipped"
    },
    {
      "id": "no_structure",
      "fired": true,
      "status": "dry_run",
      "do": "webhook",
      "connection": "feature-store",
      "preview": {
        "body": {
          "entity": "CVX",
          "as_of": "2026-10-30",
          "structure": "none"
        }
      }
    }
  ]
}

swinghigh swinglow

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Quant developer · example 17

Route a DIS drawdown signal to Slack, else to a heartbeat webhook

Wire a monitoring pattern: a real signal goes to people, every other run goes to a heartbeat endpoint so the job is known to be alive.

ora-fin-0.1.1DIS3 questions2 actions

StateDIS
Questions3 asked
Answersreal, captured
Actions2 rules · dry run

State

{"symbol":"DIS"} The server fills in the latest completed session.

Questions

`dd`	rule	`C < 0.8 * max(H, 252)` More than 20% off the 52-week high
`breakdown`	rule	`C < lowest(L, 20)` Closed under the 20-day low
`drop10`	forecast	Will it drop at least 10% at some point in the next month? A 10% drawdown within a month

Answers

Question	Answer	Reading
`dd` More than 20% off the 52-week high	0.0537	false
`breakdown` Closed under the 20-day low	0.0659	false
`drop10` A 10% drawdown within a month	0.328	33% · unlikely

Actions

Rules on the answers decide what to send.

dd >= 0.6 and drop10 >= 0.4→Slackquant-alerts

else→Webhookheartbeat

What each action would send 1 of 2 fired · dry run, nothing sent

pagerule false: skipped

heartbeatwould send (dry run)Webhook → heartbeat

{
  "job": "dd_monitor",
  "symbol": "DIS",
  "ok": true,
  "as_of": "2026-10-30"
}

What it means

For DIS on the session of 2026-10-30: Not true: More than 20% off the 52-week high; Closed under the 20-day low. Forecasts: A 10% drawdown within a month 33%. Fired: heartbeat (Webhook → heartbeat).

Exactly one branch fires per run: a page when both conditions hold, a heartbeat otherwise, so a missing heartbeat means the job failed.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "DIS"
  },
  "questions": {
    "dd": {
      "type": "yes_no",
      "ask": "C < 0.8 * max(H, 252)"
    },
    "breakdown": {
      "type": "yes_no",
      "ask": "C < lowest(L, 20)"
    },
    "drop10": {
      "type": "yes_no",
      "instructions": "Will it drop at least 10% at some point in the next month?"
    }
  },
  "actions": [
    {
      "id": "page",
      "when": "dd >= 0.6 and drop10 >= 0.4",
      "do": "slack",
      "connection": "quant-alerts",
      "message": "{{symbol}} ({{as_of}}): 20% under its 52-week high ({{dd|pct}}) with {{drop10|pct}} odds of another 10% drop."
    },
    {
      "id": "heartbeat",
      "when": "else",
      "do": "webhook",
      "connection": "heartbeat",
      "body": {
        "job": "dd_monitor",
        "symbol": "{{symbol}}",
        "ok": true,
        "as_of": "{{as_of}}"
      }
    }
  ],
  "dry_run": true
}

Response JSON (DIS, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "dd": {
      "yes_no": 0.0537,
      "rule": "C < 0.8 * max(H, 252)",
      "basis": "rule"
    },
    "breakdown": {
      "yes_no": 0.0659,
      "rule": "C < lowest(L, 20)",
      "basis": "rule"
    },
    "drop10": {
      "yes_no": 0.328
    }
  },
  "context": {
    "symbol": "DIS",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "page",
      "fired": false,
      "status": "skipped"
    },
    {
      "id": "heartbeat",
      "fired": true,
      "status": "dry_run",
      "do": "webhook",
      "connection": "heartbeat",
      "preview": {
        "body": {
          "job": "dd_monitor",
          "symbol": "DIS",
          "ok": true,
          "as_of": "2026-10-30"
        }
      }
    }
  ]
}

lowest max

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Quant developer · example 18

Moving-average family consistency on HD: sma, avg and mean

Confirm that aliases agree and that weighted and exponential averages order the way the strategy assumes.

ora-fin-0.1.1HD4 questions

StateHD
Questions4 asked
Answersreal, captured
Actionsnone

State

{"symbol":"HD"} The server fills in the latest completed session.

Questions

`sma`	rule	`C > sma(C, 50)` close above sma(C, 50)
`avg`	rule	`C > avg(C, 50)` close above avg(C, 50)
`mean`	rule	`C > mean(C, 50)` close above mean(C, 50)
`fast_slow`	rule	`ema(C, 12) > wma(C, 26)` 12-day EMA above the 26-day weighted average

Answers

Question	Answer	Reading
`sma` close above sma(C, 50)	0.8804	true
`avg` close above avg(C, 50)	0.8804	true
`mean` close above mean(C, 50)	0.8804	true
`fast_slow` 12-day EMA above the 26-day weighted average	0.9152	true

What it means

For HD on the session of 2026-10-30: True now: close above sma(C, 50); close above avg(C, 50); close above mean(C, 50); 12-day EMA above the 26-day weighted average.

The first three are the same rule under three names and must agree; if they do not, the request was malformed.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "HD"
  },
  "questions": {
    "sma": {
      "type": "yes_no",
      "ask": "C > sma(C, 50)"
    },
    "avg": {
      "type": "yes_no",
      "ask": "C > avg(C, 50)"
    },
    "mean": {
      "type": "yes_no",
      "ask": "C > mean(C, 50)"
    },
    "fast_slow": {
      "type": "yes_no",
      "ask": "ema(C, 12) > wma(C, 26)"
    }
  }
}

Response JSON (HD, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "sma": {
      "yes_no": 0.8804,
      "rule": "C > sma(C, 50)",
      "basis": "rule"
    },
    "avg": {
      "yes_no": 0.8804,
      "rule": "C > avg(C, 50)",
      "basis": "rule"
    },
    "mean": {
      "yes_no": 0.8804,
      "rule": "C > mean(C, 50)",
      "basis": "rule"
    },
    "fast_slow": {
      "yes_no": 0.9152,
      "rule": "ema(C, 12) > wma(C, 26)",
      "basis": "rule"
    }
  },
  "context": {
    "symbol": "HD",
    "as_of": "2026-10-30"
  }
}

avg ema mean sma wma

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Quant developer · example 19

Volume percentile and OBV trend features for MU

Rank today's volume within its recent history and check accumulation, as inputs to a breakout classifier.

ora-fin-0.1.1MU5 questions

StateMU
Questions5 asked
Answersreal, captured
Actionsnone

State

{"symbol":"MU"} The server fills in the latest completed session.

Questions

`vol_rank`	rule	`percentrank(V, 50) > 90` Volume in the top tenth of 50 sessions
`obv`	rule	`obv() > sma(obv(), 20)` On-balance volume above its 20-day average
`obv_en`	English → rule	Is on-balance volume higher than 20 sessions ago?
`dry`	rule	`V < 0.6 * avg(V, 20)` Volume under 60% of normal
`vol_spike`	forecast	Will volume spike tomorrow? A volume spike tomorrow

Answers

Question	Answer	Reading
`vol_rank` Volume in the top tenth of 50 sessions	0.362	false
`obv` On-balance volume above its 20-day average	0.4012	on the line (false)
`obv_en` Is on-balance volume higher than 20 sessions ago compiled to `obv() > ref(obv(), -20)`	0.1444	false
`dry` Volume under 60% of normal	0.0585	false
`vol_spike` A volume spike tomorrow	0.035	4% · unlikely

What it means

For MU on the session of 2026-10-30: Not true: Volume in the top tenth of 50 sessions; Is on-balance volume higher than 20 sessions ago; Volume under 60% of normal. On the line: On-balance volume above its 20-day average. Forecasts: A volume spike tomorrow 4%.

A high volume rank with rising OBV is participation; the next-day volume forecast is a separate, probabilistic column.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "MU"
  },
  "questions": {
    "vol_rank": {
      "type": "yes_no",
      "ask": "percentrank(V, 50) > 90"
    },
    "obv": {
      "type": "yes_no",
      "ask": "obv() > sma(obv(), 20)"
    },
    "obv_en": {
      "type": "yes_no",
      "instructions": "Is on-balance volume higher than 20 sessions ago?"
    },
    "dry": {
      "type": "yes_no",
      "ask": "V < 0.6 * avg(V, 20)"
    },
    "vol_spike": {
      "type": "yes_no",
      "instructions": "Will volume spike tomorrow?"
    }
  }
}

Response JSON (MU, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "vol_rank": {
      "yes_no": 0.362,
      "rule": "percentrank(V, 50) > 90",
      "basis": "rule"
    },
    "obv": {
      "yes_no": 0.4012,
      "rule": "obv() > sma(obv(), 20)",
      "basis": "rule"
    },
    "obv_en": {
      "yes_no": 0.1444,
      "rule": "obv() > ref(obv(), -20)",
      "basis": "pattern"
    },
    "dry": {
      "yes_no": 0.0585,
      "rule": "V < 0.6 * avg(V, 20)",
      "basis": "rule"
    },
    "vol_spike": {
      "yes_no": 0.035
    }
  },
  "context": {
    "symbol": "MU",
    "as_of": "2026-10-30"
  }
}

avg obv percentrank ref sma

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Quant developer · example 20

Pre-open sanity email for a pairs book: XLE and XOM

Before the open, check both legs of a pair for gaps and wide days and email the developer if a leg looks broken.

ora-fin-0.1.12 symbols, one request each4 questions1 actionworkflow · Mon–Fri at 08:45 ET

ScheduleMon–Fri at 08:45 ET
State2 symbols
Questions4 asked
Answersreal, captured
Actions1 rule · dry run

State

{"symbol":"XLE"} then XOM, one request each. The server fills in the latest completed session.

Questions

`gap`	rule	`abs(O - C[-1]) > 0.5 * ref(atr(14), -1)` Gap bigger than half an ATR
`wide`	rule	`(H - L) > 2 * avg(H - L, 20)` Range more than twice the 20-day average range
`big_move`	English → rule	Was today's move bigger than 1.5 ATR?
`gap_dn`	forecast	Will it gap down tomorrow? A gap down tomorrow

Answers

Symbol	`gap`rule	`wide`rule	`big_move`rule	`gap_dn`forecast	Fired
`XLE`	0.7136	0.0537	0.0975	0.062	`check_leg`
`XOM`	0.8398	0.054	0.1131	0.094	`check_leg`

true false not known for rules; forecasts are odds. Hover a column for its question.

Actions

Rules on the answers decide what to send. Each symbol runs its own request, so each one can fire.

gap >= 0.6 or wide >= 0.6→Emailme

XLE 1 of 1 fired · dry run, nothing sent

check_legwould send (dry run)Email → me

{
  "subject": "Pair leg XLE moved hard (2026-10-30)",
  "text": "XLE: gap over half an ATR 71%, wide range 5%. Check the hedge ratio before the pair trades."
}

XOM 1 of 1 fired · dry run, nothing sent

check_legwould send (dry run)Email → me

{
  "subject": "Pair leg XOM moved hard (2026-10-30)",
  "text": "XOM: gap over half an ATR 84%, wide range 5%. Check the hedge ratio before the pair trades."
}

What it means

Session of 2026-10-30, 2 symbols: Gap bigger than half an ATR: true for XLE, XOM of 2. Range more than twice the 20-day average range: true for none of 2. Was today's move bigger than 1.5 ATR: true for none of 2. Highest odds for “A gap down tomorrow”: XOM at 9%. check_leg fired for XLE, XOM.

Each leg is checked on its own; one email means the pair's spread logic should be reviewed before orders go out.

As a scheduled workflow (Mon–Fri at 08:45 ET)

Create it under Workflows in the console: it runs these questions for every symbol on the schedule and fires the actions per symbol. The answers above are what one run returned.

{
  "name": "Pre-open sanity email for a pairs book: XLE and XOM",
  "symbols": "XLE XOM",
  "days": [
    1,
    2,
    3,
    4,
    5
  ],
  "at_time": "08:45",
  "model": "ora-fin-0.1.1",
  "questions": {
    "gap": {
      "type": "yes_no",
      "ask": "abs(O - C[-1]) > 0.5 * ref(atr(14), -1)"
    },
    "wide": {
      "type": "yes_no",
      "ask": "(H - L) > 2 * avg(H - L, 20)"
    },
    "big_move": {
      "type": "yes_no",
      "instructions": "Was today's move bigger than 1.5 ATR?"
    },
    "gap_dn": {
      "type": "yes_no",
      "instructions": "Will it gap down tomorrow?"
    }
  },
  "actions": [
    {
      "id": "check_leg",
      "when": "gap >= 0.6 or wide >= 0.6",
      "do": "email",
      "connection": "me",
      "subject": "Pair leg {{symbol}} moved hard ({{as_of}})",
      "message": "{{symbol}}: gap over half an ATR {{gap|pct}}, wide range {{wide|pct}}. Check the hedge ratio before the pair trades."
    }
  ]
}

Request JSON (XLE; the others differ only in the symbol)

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "XLE"
  },
  "questions": {
    "gap": {
      "type": "yes_no",
      "ask": "abs(O - C[-1]) > 0.5 * ref(atr(14), -1)"
    },
    "wide": {
      "type": "yes_no",
      "ask": "(H - L) > 2 * avg(H - L, 20)"
    },
    "big_move": {
      "type": "yes_no",
      "instructions": "Was today's move bigger than 1.5 ATR?"
    },
    "gap_dn": {
      "type": "yes_no",
      "instructions": "Will it gap down tomorrow?"
    }
  },
  "actions": [
    {
      "id": "check_leg",
      "when": "gap >= 0.6 or wide >= 0.6",
      "do": "email",
      "connection": "me",
      "subject": "Pair leg {{symbol}} moved hard ({{as_of}})",
      "message": "{{symbol}}: gap over half an ATR {{gap|pct}}, wide range {{wide|pct}}. Check the hedge ratio before the pair trades."
    }
  ],
  "dry_run": true
}

Response JSON (XLE, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "gap": {
      "yes_no": 0.7136,
      "rule": "abs(O - C[-1]) > 0.5 * ref(atr(14), -1)",
      "basis": "rule"
    },
    "wide": {
      "yes_no": 0.0537,
      "rule": "(H - L) > 2 * avg(H - L, 20)",
      "basis": "rule"
    },
    "big_move": {
      "yes_no": 0.0975,
      "rule": "abs(C - C[-1]) > 1.5 * atr(14)",
      "basis": "pattern"
    },
    "gap_dn": {
      "yes_no": 0.062
    }
  },
  "context": {
    "symbol": "XLE",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "check_leg",
      "fired": true,
      "status": "dry_run",
      "do": "email",
      "connection": "me",
      "preview": {
        "subject": "Pair leg XLE moved hard (2026-10-30)",
        "text": "XLE: gap over half an ATR 71%, wide range 5%. Check the hedge ratio before the pair trades."
      }
    }
  ]
}

abs atr avg offset ref

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Information, not investment advice. Rules answer from the latest completed daily session; forecasts are probabilities. Actions ran as dry runs: nothing was sent. More in Builders: Fintech app builder, Robo-advisor, Broker app, Algo / bot developer, Data vendor / platform, Trading educator / newsletter.

Create an account Open the Playground Everything here also lives in the console, next to your keys and usage.

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Adaptive RSI thresholds for JPM with iif and between

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Wick geometry on XOM with least and greatest

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Typical-price and midpoint checks on IWM

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Swing-structure features for CVX with swinghigh and swinglow

State

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What it means

Route a DIS drawdown signal to Slack, else to a heartbeat webhook

State

Questions

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Actions

What it means

Moving-average family consistency on HD: sma, avg and mean

State

Questions

Answers

What it means

Volume percentile and OBV trend features for MU

State

Questions

Answers

What it means

Pre-open sanity email for a pairs book: XLE and XOM

State

Questions

Answers

Actions

What it means