audit_backtest

One-call audit of a strategy's returns. Headline: one test whose false-positive rate was measured on nine return shapes (White's Reality Check with variants, luck trials with Lo's correction without). Then deflated Sharpe, minimum track record and, with variants, overfitting and out-of-sample decay, each with its receipt, plus fix_next. Send every variant tried (variants_file) so trials are counted; point returns_file at a CSV instead of pasting. A deflated Sharpe or overfitting probability above or below any threshold is not admission to anything and is not a forecast.

Argument	Type	Description
returns	array	Periodic returns as fractions (0.01 = 1%), oldest first; replaces the Sharpe, observations, skew and kurtosis.
n_splits	integer	Even number of blocks, at least 2; default 16.
variants	array	Optional returns of every variant tried (this one included), one row per period, one column per variant; adds the overfitting check.
confidence	number	Between 0 and 1; default 0.95.
returns_file	string	Path to a CSV or JSON of the returns on this machine (not on the hosted endpoint), instead of returns.
variants_file	string	Path to a CSV or JSON with one numeric column per variant, instead of variants.
returns_column	any	Column name or 1-based position, when returns_file has several numeric columns.
periods_per_year*	number	Periods per year: 252 daily, 365 crypto, 52 weekly, 12 monthly.
benchmark_sharpe_annualized	number	Annualized Sharpe to beat; default 0.
effective_independent_trials	integer	Independent variants tried before choosing this one. Optional with variants: counted from them, and the larger number is used.
cross_trial_sharpe_sd_annualized	number	Standard deviation of annualized Sharpe across those trials. Optional with variants: measured from them, and the larger is used.

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "periods_per_year"
  ],
  "properties": {
    "returns": {
      "type": "array",
      "items": {
        "type": "number"
      },
      "maxItems": 20000,
      "minItems": 2,
      "description": "Periodic returns as fractions (0.01 = 1%), oldest first; replaces the Sharpe, observations, skew and kurtosis."
    },
    "n_splits": {
      "type": "integer",
      "description": "Even number of blocks, at least 2; default 16.",
      "exclusiveMinimum": 0
    },
    "variants": {
      "type": "array",
      "items": {
        "type": "array",
        "items": {
          "type": "number"
        },
        "maxItems": 200
      },
      "maxItems": 20000,
      "minItems": 2,
      "description": "Optional returns of every variant tried (this one included), one row per period, one column per variant; adds the overfitting check."
    },
    "confidence": {
      "type": "number",
      "description": "Between 0 and 1; default 0.95.",
      "exclusiveMaximum": 1,
      "exclusiveMinimum": 0
    },
    "returns_file": {
      "type": "string",
      "maxLength": 4096,
      "minLength": 1,
      "description": "Path to a CSV or JSON of the returns on this machine (not on the hosted endpoint), instead of returns."
    },
    "variants_file": {
      "type": "string",
      "maxLength": 4096,
      "minLength": 1,
      "description": "Path to a CSV or JSON with one numeric column per variant, instead of variants."
    },
    "returns_column": {
      "anyOf": [
        {
          "type": "string",
          "maxLength": 200,
          "minLength": 1
        },
        {
          "type": "integer",
          "minimum": 1
        }
      ],
      "description": "Column name or 1-based position, when returns_file has several numeric columns."
    },
    "periods_per_year": {
      "type": "number",
      "maximum": 10000,
      "minimum": 1,
      "description": "Periods per year: 252 daily, 365 crypto, 52 weekly, 12 monthly."
    },
    "benchmark_sharpe_annualized": {
      "type": "number",
      "maximum": 10,
      "minimum": -10,
      "description": "Annualized Sharpe to beat; default 0."
    },
    "effective_independent_trials": {
      "type": "integer",
      "maximum": 10000000,
      "minimum": 2,
      "description": "Independent variants tried before choosing this one. Optional with variants: counted from them, and the larger number is used."
    },
    "cross_trial_sharpe_sd_annualized": {
      "type": "number",
      "maximum": 10,
      "minimum": 0,
      "description": "Standard deviation of annualized Sharpe across those trials. Optional with variants: measured from them, and the larger is used."
    }
  },
  "additionalProperties": false
}

backtest_strategy

Backtest a rule on your prices over a whole parameter grid, then validate the best with the count of variants actually run: deflated Sharpe, overfitting probability, minimum track record, vs buy and hold, after costs. A backtest, deflated or not, is not a forecast and not advice.

read-only

Argument	Type	Description
grid	object	Values per parameter, e.g. {"fast":[10,20],"slow":[50,200]}; every valid combination runs (max 200).
dates	array	Optional ISO dates, one per value.
family*	string	sma_cross (fast, slow), momentum (lookback, skip), mean_reversion (window, entry_z), breakout (lookback), buy_and_hold.
prices	array	Prices, oldest first.
cost_bps	number	Cost per unit of turnover, bps; default 5.
allow_short	boolean	Short instead of flat; default false.
prices_file	string	CSV or JSON of prices on this machine; local server only.
prices_column	any	Column name or 1-based position.

check_feasibility

Check a trading plan before it trades: broker order-rate and minimum-order limits, 2026 US day-trading rules, square-root market impact with crowding, and the capital where costs eat the return. Checks the plan as described against published broker limits and a market-impact model; it never contacts a broker or sees an account.

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Argument	Type	Description
broker	string	Default other.
copies	integer	Agents trading the same signal at once; default 1.
account	string
adv_usd*	number	Daily dollar volume of a typical holding.
asset_class	string
capital_usd*	number
daily_volatility*	number	Of a typical holding, e.g. 0.02.
turnover_per_year*	number	Buys plus sells a year / capital.

check_leakage

Does a signal look ahead? plan gives cuts; rerun your code on the first cut rows for each, then compare. Rows that changed used later rows; the result names the pattern and horizon. No code is sent. A pass covers only these cuts: the columns are the caller's, and survivorship, vendor revisions or a leak no cut reaches leave no trace.

read-only

Argument	Type	Description
cuts	array	compare: plan's cuts.
seed	integer
action*	string
columns	object	compare: {name: {full: [all rows], prefixes: [[first cuts[i] rows], ...]}}, null for missing.
prefixes	integer	plan: cuts, default 5.
tolerance	number	Default 1e-9.
timestamps	array
columns_file	string	compare, on your machine: a JSON file {cuts, columns} instead.
observations	integer

placebo_test

Does your pipeline find edges in noise? plan writes placebo files (your data's returns in random orders, so nothing is predictable); run the whole pipeline on real.csv and on each, then compare for a p-value that counts every choice it makes. Beating the placebos shows the pipeline finds more in the real order of the returns than in random orders of the same returns; it does not show the edge survives costs, capacity or the future.

Argument	Type	Description
kind	string	plan: default prices.
real	number	compare: the pipeline's result on the real data.
seed	integer
action*	string
method	string
columns	object	plan: {asset: [values]} instead of data_file.
placebos	integer	plan: default 19, up to 199.
data_file	string	plan, on your machine: CSV or JSON, a column per asset, dates optional.
lower_is_better	boolean

service_status

Whether the validation API is up, with its quotas; check after a timeout before resubmitting. No key. This verdict is about the series exactly as submitted. The service never saw the data source, its costs, survivorship, or any lookahead in how the series was built.

read-only

No arguments.

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {},
  "additionalProperties": false
}

stress_test

Stress a strategy's returns: bootstrap histories (how often the drawdown limit breaks or Sharpe turns negative) and named crash, volatility, repeat and stuck-position scenarios, with a fragility share. Frequencies over resampled histories are not forecasts of the future.

read-only

Argument	Type	Description
seed	integer	Default 42; reproduces exactly.
block	number	Mean block length; default cube root of n.
crash	number	Crash size, negative; default min(-0.2, 3x worst period).
paths	integer	Default 1000.
returns	array	Strategy returns as fractions, oldest first.
returns_file	string	CSV or JSON of returns on this machine; local server only.
drawdown_limit	number	Fraction; default 0.2.
outage_periods	integer	Stuck periods after the worst; default 5.
returns_column	any

summarize_series

A long price or return series in about 100 words plus fields: growth, risk, dated drawdowns, trend, volatility regime, tails, jumps, stale data. Use instead of reading raw bars. Nothing here is a forecast or advice.

read-only

Argument	Type	Description
name	string	Label for the text.
dates	array	Optional ISO dates, one per value.
prices	array	Prices, oldest first; or returns, or series_file.
returns	array	Returns as fractions, oldest first.
series_file	string	CSV or JSON of prices or returns on this machine; local server only.
series_kind	string	series_file holds; default prices.
series_column	any	Column name or 1-based position.
periods_per_year	number	252 daily (default), 365 crypto, 52 weekly, 12 monthly.
benchmark_returns	array

validate_backtest_length

Minimum backtest length (years) before the best of N independent trials is not expected to reach a target Sharpe by luck; with backtest_years, the most trials those years allow. For planning a search; once it has a result, use validate_deflated_sharpe. A deflated Sharpe or overfitting probability above or below any threshold is not admission to anything and is not a forecast.

Argument	Type	Description
backtest_years	number	Backtest length in years, for the most trials it allows.
target_sharpe_annualized	number	In-sample annualized Sharpe you would call a discovery; default 1.
effective_independent_trials	integer	Independent trials tried (backtests, parameter sets, ideas).

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "backtest_years": {
      "type": "number",
      "maximum": 1000,
      "description": "Backtest length in years, for the most trials it allows.",
      "exclusiveMinimum": 0
    },
    "target_sharpe_annualized": {
      "type": "number",
      "maximum": 10,
      "description": "In-sample annualized Sharpe you would call a discovery; default 1.",
      "exclusiveMinimum": 0
    },
    "effective_independent_trials": {
      "type": "integer",
      "maximum": 1000000000,
      "minimum": 2,
      "description": "Independent trials tried (backtests, parameter sets, ideas)."
    }
  },
  "additionalProperties": false
}

validate_breadth

Book Sharpe ceiling from adding sleeves of this quality and correlation, the Sharpe at a sleeve count, and the sleeves a target needs. For portfolio construction; it validates no single strategy. This verdict is about the series exactly as submitted. The service never saw the data source, its costs, survivorship, or any lookahead in how the series was built.

Argument	Type	Description
target	number	Target book Sharpe, for the sleeves it needs.
sleeves	integer	Sleeve count, for that book's Sharpe.
sleeve_sharpe*	number	Annualized Sharpe of one sleeve.
average_pairwise_correlation*	number	Average correlation between sleeves, -1 to 1.

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "sleeve_sharpe",
    "average_pairwise_correlation"
  ],
  "properties": {
    "target": {
      "type": "number",
      "description": "Target book Sharpe, for the sleeves it needs.",
      "exclusiveMinimum": 0
    },
    "sleeves": {
      "type": "integer",
      "maximum": 500,
      "minimum": 1,
      "description": "Sleeve count, for that book's Sharpe."
    },
    "sleeve_sharpe": {
      "type": "number",
      "description": "Annualized Sharpe of one sleeve.",
      "exclusiveMinimum": 0
    },
    "average_pairwise_correlation": {
      "type": "number",
      "maximum": 1,
      "minimum": -1,
      "description": "Average correlation between sleeves, -1 to 1."
    }
  },
  "additionalProperties": false
}

validate_deflated_sharpe

Deflated Sharpe ratio: the probability (0 to 1) that the selected strategy's Sharpe beats the best that luck gives across the variants tried, with the probabilistic Sharpe and that luck benchmark. Send the seven statistics or a return series. With every variant's returns use validate_overfitting; luck as a trial count, validate_luck_trials; a multiple-testing haircut, validate_haircut_sharpe. A deflated Sharpe or overfitting probability above or below any threshold is not admission to anything and is not a forecast.

Argument	Type	Description
skew	number	Skewness of returns; 0 if Normal.
returns	array	Periodic returns as fractions (0.01 = 1%), oldest first; replaces the Sharpe, observations, skew and kurtosis.
observations	integer	Number of return observations.
periods_per_year	number	Periods per year: 252 daily, 365 crypto, 52 weekly, 12 monthly.
non_excess_kurtosis	number	Kurtosis, not excess kurtosis; 3 if Normal.
observed_sharpe_annualized	number	Annualized Sharpe as observed.
effective_independent_trials	integer	Independent variants tried before choosing this one.

validate_haircut_sharpe

Haircut Sharpe for multiple testing (Harvey and Liu 2015): the Sharpe a single test would have needed, by Bonferroni and independent tests, and with the other tests' Sharpes, Holm and BHY. For the probability the Sharpe is real, use validate_deflated_sharpe. A deflated Sharpe or overfitting probability above or below any threshold is not admission to anything and is not a forecast.

Argument	Type	Description
tests	integer	Tests run, this one included; gives the Bonferroni and independent-test haircuts.
observations*	integer	Return observations behind the Sharpe.
autocorrelation	number	Lag-1 autocorrelation of returns, -1 to 1; default 0. Corrects the annualized Sharpe (Lo 2002).
periods_per_year*	number	Periods per year: 252 daily, 365 crypto, 52 weekly, 12 monthly.
observed_sharpe_annualized*	number	Annualized Sharpe as observed.
other_sharpe_ratios_annualized	array	Annualized Sharpes of the other tests over the same observations; adds Holm and BHY.

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "observed_sharpe_annualized",
    "periods_per_year",
    "observations"
  ],
  "properties": {
    "tests": {
      "type": "integer",
      "maximum": 100000000,
      "minimum": 1,
      "description": "Tests run, this one included; gives the Bonferroni and independent-test haircuts."
    },
    "observations": {
      "type": "integer",
      "maximum": 1000000,
      "minimum": 3,
      "description": "Return observations behind the Sharpe."
    },
    "autocorrelation": {
      "type": "number",
      "description": "Lag-1 autocorrelation of returns, -1 to 1; default 0. Corrects the annualized Sharpe (Lo 2002).",
      "exclusiveMaximum": 1,
      "exclusiveMinimum": -1
    },
    "periods_per_year": {
      "type": "number",
      "maximum": 10000,
      "minimum": 1,
      "description": "Periods per year: 252 daily, 365 crypto, 52 weekly, 12 monthly."
    },
    "observed_sharpe_annualized": {
      "type": "number",
      "maximum": 10,
      "description": "Annualized Sharpe as observed.",
      "exclusiveMinimum": 0
    },
    "other_sharpe_ratios_annualized": {
      "type": "array",
      "items": {
        "type": "number",
        "maximum": 10,
        "minimum": -10
      },
      "maxItems": 10000,
      "minItems": 1,
      "description": "Annualized Sharpes of the other tests over the same observations; adds Holm and BHY."
    }
  },
  "additionalProperties": false
}

validate_luck_trials

How many skill-less strategies a search would need for its best to reach this Sharpe by luck (Monte Carlo), and with a trial count, the chance it did. States luck as the best of N random tries; for the probability the Sharpe is real, use validate_deflated_sharpe. A deflated Sharpe or overfitting probability above or below any threshold is not admission to anything and is not a forecast.

Argument	Type	Description
skew	number	Skewness; below -0.5 the reading warns the counts are too generous.
observations*	integer	Return observations behind the Sharpe.
autocorrelation	number	Lag-1 autocorrelation of returns, -1 to 1; default 0. Corrects the annualized Sharpe (Lo 2002).
periods_per_year*	number	Periods per year: 252 daily, 365 crypto, 52 weekly, 12 monthly.
observed_sharpe_annualized*	number	Annualized Sharpe as observed.
effective_independent_trials	integer	Independent trials tried; adds the chance the best reached this Sharpe by luck.

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "observed_sharpe_annualized",
    "periods_per_year",
    "observations"
  ],
  "properties": {
    "skew": {
      "type": "number",
      "maximum": 100,
      "minimum": -100,
      "description": "Skewness; below -0.5 the reading warns the counts are too generous."
    },
    "observations": {
      "type": "integer",
      "maximum": 1000000,
      "minimum": 3,
      "description": "Return observations behind the Sharpe."
    },
    "autocorrelation": {
      "type": "number",
      "description": "Lag-1 autocorrelation of returns, -1 to 1; default 0. Corrects the annualized Sharpe (Lo 2002).",
      "exclusiveMaximum": 1,
      "exclusiveMinimum": -1
    },
    "periods_per_year": {
      "type": "number",
      "maximum": 10000,
      "minimum": 1,
      "description": "Periods per year: 252 daily, 365 crypto, 52 weekly, 12 monthly."
    },
    "observed_sharpe_annualized": {
      "type": "number",
      "maximum": 20,
      "minimum": -20,
      "description": "Annualized Sharpe as observed."
    },
    "effective_independent_trials": {
      "type": "integer",
      "maximum": 1000000000,
      "minimum": 1,
      "description": "Independent trials tried; adds the chance the best reached this Sharpe by luck."
    }
  },
  "additionalProperties": false
}

validate_overfitting

Probability of backtest overfitting (0 to 1) by CSCV: how often the in-sample best variant falls below the out-of-sample median. Needs every variant's returns (periods by variants); with summary statistics only, use validate_deflated_sharpe. A deflated Sharpe or overfitting probability above or below any threshold is not admission to anything and is not a forecast.

Argument	Type	Description
seed	number	Sampling seed; default 42.
matrix*	array	Returns as fractions, one row per period, one column per variant.
n_splits	integer	Even number of blocks, at least 2; default 16.
max_combinations	integer	Most splits evaluated, up to 2000 (default).

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "matrix"
  ],
  "properties": {
    "seed": {
      "type": "number",
      "description": "Sampling seed; default 42."
    },
    "matrix": {
      "type": "array",
      "items": {
        "type": "array",
        "items": {
          "type": "number"
        },
        "maxItems": 200
      },
      "maxItems": 20000,
      "minItems": 2,
      "description": "Returns as fractions, one row per period, one column per variant."
    },
    "n_splits": {
      "type": "integer",
      "description": "Even number of blocks, at least 2; default 16.",
      "exclusiveMinimum": 0
    },
    "max_combinations": {
      "type": "integer",
      "maximum": 2000,
      "description": "Most splits evaluated, up to 2000 (default).",
      "exclusiveMinimum": 0
    }
  },
  "additionalProperties": false
}

validate_paper_evidence

Checks paper-evidence.v0 structure and disclosures. With CANLI_LOCAL=1, record_file reads an export bundle and journal_file verifies signatures, source hashes and recomputed claims. Without a journal, source facts and signatures are unchecked. Local files are never uploaded. This verdict is about the series exactly as submitted. The service never saw the data source, its costs, survivorship, or any lookahead in how the series was built.

Argument	Type	Description
record	object	A canli.paper-evidence.v0 record.
signature	object	Detached record signature for a signed inline record; requires journal_file.
record_file	string	Local record or export-bundle JSON path; requires CANLI_LOCAL=1.
journal_file	string	Local signed journal to recompute and bind; requires CANLI_LOCAL=1.

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "record": {
      "type": "object",
      "description": "A canli.paper-evidence.v0 record."
    },
    "signature": {
      "type": "object",
      "required": [
        "scheme",
        "public_key",
        "signature"
      ],
      "properties": {
        "scheme": {
          "type": "string",
          "const": "Ed25519"
        },
        "signature": {
          "type": "string",
          "maxLength": 128
        },
        "public_key": {
          "type": "string",
          "maxLength": 64
        }
      },
      "description": "Detached record signature for a signed inline record; requires journal_file.",
      "additionalProperties": false
    },
    "record_file": {
      "type": "string",
      "maxLength": 1000,
      "minLength": 1,
      "description": "Local record or export-bundle JSON path; requires CANLI_LOCAL=1."
    },
    "journal_file": {
      "type": "string",
      "maxLength": 1000,
      "minLength": 1,
      "description": "Local signed journal to recompute and bind; requires CANLI_LOCAL=1."
    }
  },
  "additionalProperties": false
}

validate_reality_check

Data-snooping tests on every variant a search tried: Hansen's SPA p-value that the best beat the benchmark only by luck, White's Reality Check, and the variants Romano-Wolf StepM finds better. Send all variants tried, not only the winners. A deflated Sharpe or overfitting probability above or below any threshold is not admission to anything and is not a forecast.

Argument	Type	Description
reps	integer	Bootstrap draws; default 2000.
seed	integer	Sampling seed; default 42.
alpha	number	Familywise error for StepM; default 0.05.
matrix	array	Returns of every variant the search tried, one row per period, one column per variant.
benchmark	array	Benchmark return per period; default zero.
matrix_file	string	Path to a CSV or JSON with one numeric column per variant, instead of matrix.
block_length	integer	Mean bootstrap block in periods; default round(n^(1/3)).

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "reps": {
      "type": "integer",
      "maximum": 400000,
      "minimum": 500,
      "description": "Bootstrap draws; default 2000."
    },
    "seed": {
      "type": "integer",
      "description": "Sampling seed; default 42."
    },
    "alpha": {
      "type": "number",
      "maximum": 0.5,
      "description": "Familywise error for StepM; default 0.05.",
      "exclusiveMinimum": 0
    },
    "matrix": {
      "type": "array",
      "items": {
        "type": "array",
        "items": {
          "type": "number"
        },
        "maxItems": 200
      },
      "maxItems": 20000,
      "minItems": 30,
      "description": "Returns of every variant the search tried, one row per period, one column per variant."
    },
    "benchmark": {
      "type": "array",
      "items": {
        "type": "number"
      },
      "maxItems": 20000,
      "minItems": 30,
      "description": "Benchmark return per period; default zero."
    },
    "matrix_file": {
      "type": "string",
      "maxLength": 4096,
      "minLength": 1,
      "description": "Path to a CSV or JSON with one numeric column per variant, instead of matrix."
    },
    "block_length": {
      "type": "integer",
      "maximum": 10000,
      "minimum": 1,
      "description": "Mean bootstrap block in periods; default round(n^(1/3))."
    }
  },
  "additionalProperties": false
}

validate_track_record

Minimum track record length (observations and years) for an observed Sharpe to beat a benchmark at a confidence level; with observations, the record's probabilistic Sharpe so far. For live or paper records; to size a backtest for its trials, use validate_backtest_length. A deflated Sharpe or overfitting probability above or below any threshold is not admission to anything and is not a forecast.

Argument	Type	Description
skew*	number	Skewness of returns; 0 if Normal.
confidence	number	Between 0 and 1; default 0.95.
observations	integer	Record length so far, for its probabilistic Sharpe.
periods_per_year*	number	Periods per year: 252 daily, 365 crypto, 52 weekly, 12 monthly.
non_excess_kurtosis*	number	Kurtosis, not excess kurtosis; 3 if Normal.
observed_sharpe_annualized*	number	Annualized Sharpe as observed.
benchmark_sharpe_annualized	number	Annualized Sharpe to beat; default 0.

Input schema (JSON)

verify_receipt

Verify a receipt offline: its Ed25519 signature against the bundled canlicapital.com key, its output hash and its id. Send an id to fetch it first, or the receipt itself. The receipt is content-hashed, reproducible from the open-source core it names, and signed with Ed25519 by a key published at https://canlicapital.com/.well-known/canli-receipt-keys.json.

read-only

Argument	Type	Description
id	string	Receipt id from a validation result.
receipt	object	A receipt object, to verify without fetching.
include_receipt	boolean	With id: also return the stored receipt.

Input schema (JSON)

{
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "id": {
      "type": "string",
      "pattern": "^[0-9a-f]{24}$",
      "description": "Receipt id from a validation result."
    },
    "receipt": {
      "type": "object",
      "description": "A receipt object, to verify without fetching."
    },
    "include_receipt": {
      "type": "boolean",
      "description": "With id: also return the stored receipt."
    }
  },
  "additionalProperties": false
}

Canli Validation

Try it

audit_backtest

Canli Validation

Try it

audit_backtest

Tools (18)