How does this skill prevent market impact during large crypto trades?

This skill mitigates price slippage and front-running by programmatically slicing large orders into smaller tranches using TWAP, VWAP, and Iceberg logic, ensuring institutional-sized trades don't disrupt low-liquidity markets.

Which AI agent frameworks and exchanges are compatible with these algorithms?

The skill is designed for Python-based trading agents and is optimized for the Binance API ecosystem, covering both Spot and Futures markets.

What specific assets are included in the purchase of this skill?

You receive the complete quantitative logic for the execution strategies, including liquidity analysis modules, friction modeling formulas, and decimal precision handlers ready for integration.

Why should I use this skill instead of prompting an AI to write a trading script?

Unlike standard LLMs that struggle with floating-point errors, this skill uses strict decimal arithmetic to meet exchange LOT_SIZE requirements, preventing API rejects caused by scientific notation or rounding issues.

Do these algorithms require external data feeds to function correctly?

The skill requires a live data connection to Binance (Spot or Futures) to ingest real-time order book depth and mid-price data for its liquidity calculations.

Can I estimate my total execution costs before the trade is fully executed?

Yes, the skill includes friction modeling logic that calculates estimated basis point (bps) drag, factoring in exchange fees and half-spreads before you commit capital to a trade.

Algorithmic Execution & Smart Order Routing

Example session with this skill installed

Calculate a TWAP execution plan for buying 500,000 RESOLV tokens over 60 minutes. The mid-price is 0.045 USDT, and Depth 1% is $12,000 USDT. Factor in a 20 bps fee.

Read your context and instructions
Compiled the algorithmic execution

I have calculated the TWAP execution strategy for your RESOLV order. To minimize slippage, the plan uses 12 five-minute intervals and strictly adheres to liquidity-aware slice sizing.

TWAP Execution Plan: RESOLV/USDT

Metric	Value
Total Order Notional	22,500.00 USDT
Target Duration	60 Minutes
Interval Frequency ($\Delta t$)	5 Minutes
Total Slices (N)	12
Max Allowable Slice	600.00 USDT (5% of Depth 1%)
Calculated Slice Size ($q_i$)	1,875.00 USDT
Friction Estimate (bps)	35.00 bps

Strategy Overrides

Because the calculated slice (1,875 USDT) exceeds the safety threshold of 600 USDT (5% of $12k Depth), I recommend increasing the execution window to 188 minutes or splitting the trade across 38 slices to remain below the 80 bps market impact curve.

Execution Parameters (Python Decimal)

from decimal import Decimal

# Precision-safe parameters for RESOLV
tick_size = Decimal('0.00000100')
lot_size = Decimal('1.00000000')
min_notional = Decimal('5.00')

# Slice 01 target
price = Decimal('0.04500000').quantize(tick_size)
quantity = Decimal('13333.00000000').quantize(lot_size)
notional = (price * quantity)

print(f"Price: {price:.8f}")
print(f"Qty: {quantity:.8f}")
print(f"Total Notional: {notional:.2f} USDT")

Next steps

Pull the current PRICE_FILTER from the Binance exchangeInfo endpoint to verify tick precision.
Confirm if the execution window can be extended to 190 minutes to reduce market impact.
Verify that the 20 bps fee tier matches your current Binance VIP level.

Connects securely to your tools. The creator never sees your data.

About this skill

Large orders in low-liquidity crypto markets suffer from aggressive slippage and front-running. This skill implements quantitative execution strategies to minimize market impact and manage order book depth. It provides logic for slicing institutional-sized trades into manageable tranches across Binance Spot and Futures markets.

What it does

Execution Slicing implements TWAP, VWAP, and Iceberg architectures to distribute order size over time.
Liquidity Analysis calculates touch spreads and cumulative depth bands at 1% and 2% intervals from mid-price.
Friction Modeling estimates net execution costs including exchange fees, half-spreads, and market impact.
Precision Management handles sub-cent tick sizes and decimal quantization for micro-cap assets.
Slippage Mitigation enforces notional limits based on real-time order book depth to prevent price spikes.

How it works

Analyze Depth by summing bid/ask notionals within normalized price bands relative to the mid-price.
Select Strategy between time-weighted, volume-weighted, or hidden iceberg slices based on trade urgency.
Calculate Slices using decimal arithmetic to ensure compliance with exchange LOT_SIZE and PRICE_FILTER rules.
Model Friction to estimate total basis point (bps) drag before committing capital.

Frameworks & tools

Python Decimal library, Binance API standards (Spot and Futures), and quantitative finance models for market impact and volume distribution.

Why this beats prompting it yourself

Standard LLM prompts often fail at high-precision decimal arithmetic and scientific notation errors like "0E-8" which crash exchange APIs. This skill provides specific mathematical formulas for liquidity-aware sizing that basic prompting ignores.

Use cases

Micro-cap Accumulation of illiquid tokens without spiking the entry price.
Institutional Offloading of large positions using VWAP to match historical volume curves.
Hidden Liquidity deployment via Iceberg orders to prevent adverse front-running.

Known limitations

Requires external price and depth data feeds from Binance APIs to calculate real-time bands. Only supports USDT-denominated notional calculations.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Algorithmic Execution & Smart Order Routing