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Agent skill

Backtest Execution Modeling

by HKUDS in HKUDS/Vibe-Trading

Adds realistic execution assumptions to backtests: fixed, linear and square-root slippage, delayed fills, VWAP and TWAP logic and market-impact cost estimates; never for live orders.

MITAuto-check passedBusiness, Finance & HR

Install Backtest Execution Modeling

skills CLI
$ npx skills add HKUDS/Vibe-Trading --skill execution-model -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install HKUDS/Vibe-Trading execution-model --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agent/src/skills/execution-model .claude/skills/execution-model && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
execution-model
GitHub stars
35k
Token cost
~2.9k tokens
SKILL.md length
729 words
Files
1
Skills in repo
89
Repo updated
First seen
Licence
MIT

At a glance

Adds realistic execution assumptions to backtests: fixed, linear and square-root slippage, delayed fills, VWAP and TWAP logic and market-impact cost estimates; never for live orders.

  • Works in 3 steps: Fixed Slippage Model → Linear Impact Model → Square-Root Impact Model
  • Adding slippage to a backtest that currently fills at the close
  • SKILL.md covers Overview, Slippage Models, Execution Algorithm Principles and Integrated Transaction-Cost…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Backtests that fill at the close with zero slippage overstate returns, so this skill supplies more realistic execution assumptions for simulation only; it does not place live orders. Four tested functions in src/quantlib/impact.py cover fixed slippage, linear impact, square-root impact and delayed execution, and the agent is told to import them instead of retyping the formulas, since they validate inputs such as a zero average daily volume or a negative bar delay.

Reference tables give suggested fixed slippage in basis points by market, from US large caps and A-share large caps to Hong Kong stocks, BTC and ETH spot and small altcoins, along with impact coefficients for the linear model. The skill notes that linear impact treats marginal cost as constant and so overstates very large orders, and it also covers VWAP and TWAP execution logic and how to configure execution assumptions.

When your agent uses it

  • Adding slippage to a backtest that currently fills at the close
  • Choosing slippage assumptions for A-shares, US stocks or crypto pairs
  • Estimating market-impact cost for a large order relative to average daily volume
  • Modeling delayed execution without introducing look-ahead bias

Example prompts

  • “Add 5 bps of fixed slippage to my backtest fills.”
  • “Estimate the linear impact cost of trading 100k shares against 1M average daily volume.”
  • “Which slippage in basis points suits CSI 300 stocks versus small caps?”
  • “Switch my backtest from fixed slippage to square-root impact.”

Requirements

  • The impact functions in src/quantlib/impact.py

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Fixed Slippage Model
  2. Linear Impact Model
  3. Square-Root Impact Model

What it can do on your machine

Read from SKILL.md and the folder at commit 7f6908b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python, json and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Backtest Execution Modeling loads about 2.9k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 729 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from HKUDS/Vibe-Trading at commit 7f6908b, republished under its MIT licence (© HKUDS). 729 words, ~2,886 tokens.

Download SKILL.mdSave it as .claude/skills/execution-model/SKILL.md (or your agent's skills folder).
name
execution-model
description
Trade execution modeling (backtest only) — slippage formulas (linear / square-root impact), VWAP/TWAP execution logic, market-impact cost estimation, and execution-assumption configuration.
category
strategy

Trade Execution Modeling

Overview

Provide more realistic execution assumptions for backtests, including slippage models, market-impact estimation, and execution-algorithm principles. This skill is for backtest simulation only and does not involve live order execution.

Slippage Models

Why Slippage Models Are Needed
Idealized backtest: filled at the close, zero slippage
Real world:
1. The order book has a bid-ask spread
2. Large orders push prices (market impact)
3. Execution is delayed (there is latency from signal to fill)

No slippage model -> overly optimistic backtest -> losses in live trading

Do not retype these models. All four are implemented and tested in src/quantlib/impact.py; import them. The tested versions validate their inputs — a zero ADV raises instead of dividing by zero, and a negative delay_bars raises instead of silently introducing look-ahead bias.

python
from src.quantlib.impact import fixed_slippage, linear_impact, sqrt_impact, delayed_execution
1. Fixed Slippage Model
python
fixed_slippage(price=100.0, direction=1, bps=5.0)   # 100.05  (buy pays up)
fixed_slippage(price=100.0, direction=-1, bps=5.0)  #  99.95  (sell receives less)

direction is 1 to buy or -1 to sell, and must be exactly one of those — it multiplies the impact, so an unchecked 2 would silently double the modelled cost. bps defaults to DEFAULT_SLIPPAGE_BPS (5.0).

Reference fixed-slippage assumptions by market:

MarketInstrumentSuggested Slippage (bps)Notes
China A-share large capCSI 300 constituents3-5Good liquidity
China A-share small capCSI 1000 constituents5-10Average liquidity
China micro-capmarket cap < 5 billion RMB10-30Poor liquidity
US large capAAPL / MSFT1-3Excellent liquidity
Hong Kong stocksHang Seng constituents5-10Less liquid than A / US
BTC spotBTC-USDT2-5Good OKX liquidity
ETH spotETH-USDT3-8Slightly worse than BTC
Small altcoinsother -USDT pairs10-50Liquidity varies widely
2. Linear Impact Model

impact = impact_coeff × volume_traded / adv

python
# 100k shares against 1M ADV = 10% participation; at coeff 0.1 that is a 1% move.
linear_impact(price=100.0, direction=1, volume_traded=100_000, adv=1_000_000, impact_coeff=0.1)
# 101.0

Marginal impact is constant here, which overstates the cost of very large orders. impact_coeff defaults to DEFAULT_LINEAR_IMPACT_COEFF (0.1).

Reference impact coefficients:

Marketimpact_coeffNotes
China A-share large cap0.05-0.1010% daily price-limit system
China A-share small cap0.10-0.20Liquidity premium
US equities0.03-0.08Market-maker buffering
Crypto0.05-0.1524h trading is dispersed
3. Square-Root Impact Model

impact = η × σ × sqrt(volume_traded / adv)

python
# 250k against 1M ADV = 25% participation; 0.5 × 0.02 × sqrt(0.25) = 0.005 = 50bps.
sqrt_impact(price=100.0, direction=1, volume_traded=250_000, adv=1_000_000,
            volatility=0.02, eta=0.5)
# 100.5  (100.49999999999999 in binary floating point)

volatility is daily return volatility as a decimal fraction. eta defaults to DEFAULT_SQRT_IMPACT_ETA (0.5); 0.3-0.8 is the usual calibrated range.

Advantages of the square-root model:

  • Strongest empirical support (standard in financial literature)
  • Marginal impact declines for larger orders (intuitive)
  • Parameters can be estimated from historical data

Naming. This impact term is often labelled "Almgren-Chriss", and it does come from that literature, but it is not Almgren-Chriss optimal execution. There is no trading trajectory, no permanent/temporary impact split and no risk-aversion parameter here, and none is implemented anywhere in this repository. Call it a square-root impact function, and do not claim an optimal schedule was computed.

Slippage Model Selection Decision Tree
Backtest capital vs instrument ADV:
├── Capital < 0.5% of ADV -> fixed slippage (5bps) is enough
├── Capital 0.5-5% -> linear impact model
└── Capital > 5% -> square-root impact model (required)

Execution Algorithm Principles

VWAP (Volume Weighted Average Price)
Goal: execute at the day's volume-weighted average price

VWAP = Σ(Price_i × Volume_i) / Σ(Volume_i)

Execution logic:
1. Forecast the intraday volume profile (typically U-shaped)
2. Split the order according to the predicted profile
3. Execute proportionally in each time slice

Typical China A-share VWAP volume profile (U-shaped):
09:30-10:00  15%  (active open)
10:00-11:30  25%  (normal morning session)
13:00-14:00  15%  (weak afternoon session)
14:00-14:30  15%  (afternoon recovery)
14:30-15:00  30%  (active close)

VWAP in backtests:
- Daily backtest: use the VWAP field directly as the fill price
- Minute backtest: simulate VWAP order slicing
TWAP (Time Weighted Average Price)
Goal: execute evenly over a specified time window

TWAP = simple time-sliced execution

Execution logic:
1. Define an execution window (for example 09:30-11:30)
2. Divide it into N time buckets
3. Execute total_size / N in each bucket

Pros and cons:
+ Simple, no need to forecast volume
- Easier to cause impact during low-volume periods
- Less adaptive than VWAP
Simulating Execution Delay in Backtests
python
signals = delayed_execution(raw_signal, delay_bars=1)   # T+1: trade tomorrow on today's signal
signals = delayed_execution(raw_signal, delay_bars=0)   # same-bar execution
  • China A-shares: delay_bars=1 (T+1 rule)
  • Crypto: delay_bars=0 or 1

A negative delay_bars raises. It would pull future signal values into the past, which is look-ahead bias and silently inflates every backtest containing it — the tested implementation refuses rather than letting that pass unnoticed.

Show full SKILL.md (285 more words)Show less

Integrated Transaction-Cost Model

Total Cost Breakdown
Total trading cost = explicit cost + implicit cost

Explicit cost:
- Commission: China A-shares 2-3 bps, crypto 0.02-0.1%
- Stamp duty (China A-share sell side): 0.05% (sell orders only)
- Transfer fee: negligible

Implicit cost:
- Bid-ask spread: 0.5-5bps
- Market impact: depends on trade size and liquidity
- Opportunity cost: loss from not filling at the best price
Reference Trading Costs by Market
Cost ItemChina A-sharesHong KongUSCrypto (OKX)
Commission (one way)0.025%0.05%0 (zero commission)0.08% (maker)
Stamp duty0.05% (sell)0.1% (both sides)00
Bid-ask spread0.03-0.1%0.05-0.2%0.01-0.05%0.01-0.05%
Total one-way~0.1%~0.2%~0.03%~0.1%
Total round-trip~0.2%~0.4%~0.06%~0.2%
Cost Settings in Backtests
json
{
  "commission": 0.001,
  "comment": "0.1% one-way commission, already includes stamp duty and spread"
}

Recommendations:

  • China A-shares: commission = 0.001 (conservative, includes all costs)
  • Crypto: commission = 0.001 (including slippage)
  • Hong Kong / US equities: commission = 0.001-0.002

Backtest Execution Assumptions

Relevant config.json Settings
json
{
  "commission": 0.001,
  "engine": "daily",
  "interval": "1D"
}
Advanced Execution Assumptions (implemented in signal_engine.py)
python
from src.quantlib.impact import delayed_execution


class SignalEngine:
    def __init__(self):
        # Execution assumption parameters
        self.execution_delay = 1       # T+1 delay
        self.slippage_bps = 5          # Fixed 5bps slippage
        self.max_participation = 0.05  # Maximum participation rate 5%

    def generate(self, data_map):
        for code, df in data_map.items():
            # 1. Generate raw signal
            raw_signal = self._compute_signal(df)

            # 2. Apply execution delay
            delayed_signal = delayed_execution(raw_signal, self.execution_delay)

            # 3. Apply volume filter (do not trade when liquidity is too low)
            volume_ok = df['volume'] > df['volume'].rolling(20).mean() * 0.3
            delayed_signal[~volume_ok] = 0

            signals[code] = delayed_signal

Analysis Framework

Evaluate the Impact of Transaction Costs
Step 1: Estimate annual turnover
  Annual turnover = annual trade count × 2 (buy + sell) / number of positions

Step 2: Compute annual cost drag
  Annual cost = annual turnover × total one-way cost

Step 3: Evaluate the impact on returns
  Net return = gross return - annual cost

Example:
  Annual turnover = 12 (monthly rebalance)
  One-way cost = 0.1%
  Annual cost = 12 × 0.1% = 1.2%
  If annualized return is only 5% -> costs eat 24% of returns!
Sensitivity Analysis for Execution Assumptions
markdown
### Backtest Results Under Different Slippage Assumptions

| Slippage (bps) | Annual Return | Sharpe | Max Drawdown |
|-----------|---------|--------|---------|
| 0 (ideal) | 15.2% | 1.35 | -18.5% |
| 3 | 13.8% | 1.22 | -19.0% |
| 5 | 12.9% | 1.15 | -19.2% |
| 10 | 11.1% | 0.98 | -19.8% |
| 20 | 7.5% | 0.65 | -20.5% |

Conclusion: the strategy still has meaningful profitability under 10bps slippage

Output Format

markdown
## Execution Cost Analysis

### Strategy Trading Characteristics
| Metric | Value |
|------|-----|
| Average annual trade count | 48 |
| Annual turnover | 4.8x |
| Average holding days | 25 |
| Average order size | ¥50,000 |

### Cost Estimate
| Cost Item | Per Trade | Annualized |
|--------|------|------|
| Commission | 0.025% | 0.24% |
| Stamp duty | 0.025% | 0.12% |
| Estimated slippage | 0.03% | 0.29% |
| **Total** | **0.08%** | **0.65%** |

### Cost Impact
- Gross return: 12.5%
- Net return: 11.85%
- Cost drag: -0.65% (5.2% of gross return)
- Conclusion: cost impact is manageable

### Optimization Suggestions
1. Lower turnover (lengthen holding period)
2. Avoid trading during low-liquidity windows
3. Use limit orders instead of market orders

Notes

  1. Backtest only: this system does not execute live trades; the execution model is used only to improve backtest realism
  2. Conservative assumptions: in backtests, it is better to overestimate transaction costs than to underestimate them
  3. China A-share T+1 rule: trades cannot be executed on the same day the signal is generated, so execution must be delayed by 1 day
  4. Price-limit constraints: when China A-shares are locked at limit-up / limit-down, no fill is possible; those dates should be skipped in backtests
  5. Volume constraints: order size should not exceed 5-10% of the day’s traded volume, otherwise the impact model becomes invalid
  6. Backtest overfitting: even with slippage included, the strategy may still overfit; out-of-sample validation matters more
  7. commission in config: the default 0.001 (0.1%) is a reasonable all-in cost estimate
  8. The models are implemented, not improvised: src/quantlib/impact.py holds all four, tested. Import them rather than retyping; the tested versions reject a zero ADV, a negative order size and a negative execution delay, all of which the retyped versions used to accept silently

© HKUDS, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in agent/src/skills/execution-model of HKUDS/Vibe-Trading.

Open the folder on GitHubat commit 7f6908b

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Works with

Categories

Questions about Backtest Execution Modeling

What does Backtest Execution Modeling do?

Adds realistic execution assumptions to backtests: fixed, linear and square-root slippage, delayed fills, VWAP and TWAP logic and market-impact cost estimates; never for live orders. Backtests that fill at the close with zero slippage overstate returns, so this skill supplies more realistic execution assumptions for simulation only; it does not place live orders.py cover fixed slippage, linear impact, square-root impact and delayed execution, and the agent is told to import them instead of retyping the formulas, since they validate inputs such as a zero average daily volume or a negative bar delay.

When should I use Backtest Execution Modeling?

Backtest Execution Modeling fits situations like: adding slippage to a backtest that currently fills at the close; choosing slippage assumptions for A-shares, US stocks or crypto pairs; estimating market-impact cost for a large order relative to average daily volume; modeling delayed execution without introducing look-ahead bias.

How do I install Backtest Execution Modeling in Claude Code?

Run `npx skills add HKUDS/Vibe-Trading --skill execution-model -a claude-code`. Or copy the skill folder (agent/src/skills/execution-model in HKUDS/Vibe-Trading) into .claude/skills/execution-model in your project. Claude Code loads it when a task matches its description.

How do I install Backtest Execution Modeling in Codex?

Run `npx skills add HKUDS/Vibe-Trading --skill execution-model -a codex`. Or copy the skill folder (agent/src/skills/execution-model in HKUDS/Vibe-Trading) into .agents/skills/execution-model in your project. Codex loads it when a task matches its description.

Can I use Backtest Execution Modeling in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HKUDS/Vibe-Trading --skill execution-model -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/execution-model, .gemini/skills/execution-model, .github/skills/execution-model and .opencode/skills/execution-model in your project.

What does Backtest Execution Modeling need to run?

SKILL.md names no scripts, command-line tools or credentials: Backtest Execution Modeling is instructions for the agent only. Our summary lists: The impact functions in src/quantlib/impact.py.

Does Backtest Execution Modeling access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Backtest Execution Modeling safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Backtest Execution Modeling use?

Backtest Execution Modeling is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Backtest Execution Modeling use?

About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Backtest Execution Modeling?

Skills that share tags, products or a category with Backtest Execution Modeling: CCXT Crypto Exchange Library (2025Emma/vibe-coding-cn, 23k stars), TqSdk Trading and Data (shinnytech/tqsdk-python, 5.1k stars), Stock Deep Analysis Workflow (wbh604/UZI-Skill, 7.1k stars) and Tushare Data (zillionare/zillionare, 318 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Backtest Execution Modeling?

HKUDS (a GitHub organization) maintains it in HKUDS/Vibe-Trading, which has 34,884 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 6, 2026.

Source: HKUDS/Vibe-Trading on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.