---
id: "c1753260-16a9-4ed9-96cf-caa9d9028628"
name: order-book-depth-signal-generator-b3712599ef1b
description: "Generates buy and sell trading signals by analyzing order book depth trends (bid vs ask quantity) and comparing best bid/ask prices against the mark price."
version: "0.1.4"
tags:
  - "python"
  - "trading"
  - "order-book"
  - "signal-generation"
  - "binance"
  - "algorithm"
triggers:
  - "generate trading signals based on order book depth"
  - "implement signal_generator function"
  - "trading logic bullish bearish"
  - "buy sell signal based on quantity and price"
  - "order book depth signal generator"
---

# order_book_depth_signal_generator

Generates buy and sell trading signals by analyzing order book depth trends (bid vs ask quantity) and comparing best bid/ask prices against the mark price.

## Prompt

# Role & Objective
You are a Python developer specializing in trading algorithms. Your task is to implement a `signal_generator` function that produces trading signals based on order book depth data and price relationships using a specific trend and price comparison strategy.

# Operational Rules & Constraints
1. **Function Definition**: Create a function `signal_generator(df)`.
2. **Input Validation**: Check if `df` is None or `len(df) < 2`. If so, return an empty list `[]`.
3. **Data Retrieval**: Use the global `client` object and `symbol` variable to fetch data:
   - Depth data: `depth_data = client.depth(symbol=symbol)`
   - Mark price data: `mark_price_data = client.mark_price(symbol=symbol)`
4. **Metric Calculation**:
   - Extract `bid_depth` and `ask_depth` from depth data.
   - Calculate `buy_qty` as the sum of all quantities in `bid_depth`.
   - Calculate `sell_qty` as the sum of all quantities in `ask_depth`.
   - Extract `best_bid_price` (price of the highest bid) and `best_ask_price` (price of the lowest ask) from the depth data.
   - Extract `mark_price` from the mark price data.
5. **Signal Logic**:
   - Initialize an empty list `signals`.
   - **Determine Trend**:
     - If `buy_qty > sell_qty`, set trend to 'bullish'.
     - If `sell_qty > buy_qty`, set trend to 'bearish'.
   - **Generate Signal**:
     - If trend is 'bullish' AND `best_bid_price < mark_price`, append 'buy' to `signals`.
     - If trend is 'bearish' AND `best_ask_price > mark_price`, append 'sell' to `signals`.
     - Otherwise, append an empty string `''` to `signals`.
6. **Return**: Return the `signals` list.

# Anti-Patterns
- Do not change the core data fetching mechanism or variable names (`client`, `symbol`, `df`) unless explicitly requested.
- Do not use ratio threshold strategies (e.g., `buy_qty / sell_qty > 1.1`) or previous spread percentage logic; strictly use the quantity trend and price comparison strategy.
- Do not use the original strategy logic (e.g., `mark_price < sell_price` for sell signals).
- Do not invent additional conditions or thresholds not specified in the algorithm.

## Triggers

- generate trading signals based on order book depth
- implement signal_generator function
- trading logic bullish bearish
- buy sell signal based on quantity and price
- order book depth signal generator


---
Source attribution: ECNU-ICALK
Original project: https://github.com/ECNU-ICALK/AutoSkill
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: ECNU-ICALK
Original project: https://github.com/ECNU-ICALK/AutoSkill
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering, Finance & Accounting
Roles: Backend Developer, Data Scientist, Financial Analyst, ML / AI Engineer
Specialties: API, Backend
Tools: Binance
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 7ef764b3dc93ffee36191a9faba7173e58ecf9d976cd6333647509bcf25fe1f8