/tradememory-bridge

name: tradememory-bridge
description: |
  Bridge between Binance trading events and TradeMemory Protocol.
  Automatically journals trades, recalls similar past setups, detects behavioral biases,
  and provides outcome-weighted recall for AI trading agents.
  Use this skill after executing Binance spot trades to build persistent memory.
metadata:
  version: "1.0"
  author: mnemox-ai
license: MIT

TradeMemory Bridge for Binance

Store Binance spot trades into persistent memory. Recall similar past trades before entering new positions. Detect behavioral biases (overtrading, revenge trading). Track strategy performance across sessions.

**Requires**: [TradeMemory Protocol](https://github.com/mnemox-ai/tradememory-protocol) MCP server running.

Setup

Install and start the TradeMemory MCP server:

pip install tradememory-protocol
python -m tradememory

Or add to Claude Desktop / Claude Code MCP config:

{
  "mcpServers": {
    "tradememory": {
      "command": "uvx",
      "args": ["tradememory-protocol"]
    }
  }
}

Workflow

After executing a Binance spot trade using the Binance Spot skill:

1. **Store the trade** using `remember_trade` MCP tool 2. **Before next trade**, recall similar past trades using `recall_memories` MCP tool 3. **Check agent state** using `get_agent_state` to see if drawdown or confidence suggests pausing 4. **Review behaviors** using `get_behavioral_analysis` to detect biases

MCP Tools Reference

remember_trade

Store a completed trade into memory. Automatically updates all memory layers.

**Parameters:**

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | symbol | string | Yes | Trading pair (e.g. "BTCUSDT", "ETHUSDT") | | direction | string | Yes | "long" or "short" | | entry_price | number | Yes | Entry price | | exit_price | number | Yes | Exit price | | pnl | number | Yes | Profit/loss in account currency | | strategy_name | string | Yes | Strategy name (e.g. "GridBreakout", "MeanReversion") | | market_context | string | Yes | Natural language description of market conditions | | pnl_r | number | No | P&L as R-multiple (risk units) | | context_regime | string | No | Market regime: trending_up, trending_down, ranging, volatile | | confidence | number | No | Confidence level 0-1 (default 0.5) | | reflection | string | No | Lessons learned from this trade |

**Example — after a Binance spot BUY→SELL cycle:**

Call remember_trade with:
  symbol: "BTCUSDT"
  direction: "long"
  entry_price: 87500.00
  exit_price: 89200.00
  pnl: 170.00
  strategy_name: "BreakoutEntry"
  market_context: "BTC broke above 87000 resistance with volume spike. Funding rate positive. 4H RSI was 62."
  context_regime: "trending_up"
  confidence: 0.7
  reflection: "Entry timing was good. Could have held longer — exited at first pullback."

recall_memories

Before entering a new trade, recall past trades in similar market conditions. Returns scored results ranked by outcome quality and context similarity.

**Parameters:**

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | symbol | string | Yes | Trading pair to filter by | | market_context | string | Yes | Current market conditions (natural language) | | context_regime | string | No | Current regime: trending_up, trending_down, ranging, volatile | | strategy_name | string | No | Filter by strategy | | limit | number | No | Max results (default 10) |

**Example — before entering a new BTC trade:**

Call recall_memories with:
  symbol: "BTCUSDT"
  market_context: "BTC consolidating near 90000 after rally. Volume declining. Funding rate turning negative."
  context_regime: "ranging"
  strategy_name: "BreakoutEntry"
  limit: 5

Returns past trades ranked by relevance to current conditions, with per-trade scores.

get_agent_state

Check current trading state: confidence, risk appetite, drawdown, win/loss streaks.

**No parameters required.**

Returns a recommended action: `normal`, `reduce_size`, or `stop_trading` based on drawdown severity.

get_behavioral_analysis

Detect trading biases from historical behavior patterns.

**Parameters:**

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | strategy_name | string | No | Filter by strategy | | symbol | string | No | Filter by symbol |

Detects: overtrading, revenge trading (re-entry after loss), disposition effect (cutting winners too early, holding losers too long), lot sizing inconsistency.

get_strategy_performance

Get win rate, profit factor, and aggregate stats per strategy.

**Parameters:**

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | strategy_name | string | No | Filter by strategy | | symbol | string | No | Filter by symbol |

create_trading_plan

Set conditional plans that trigger on specific market conditions.

**Parameters:**

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | trigger_type | string | Yes | "market_condition", "drawdown", or "time_based" | | trigger_condition | string | Yes | JSON describing when to trigger | | planned_action | string | Yes | JSON describing what to do | | reasoning | string | Yes | Why this plan was created |

**Example:**

Call create_trading_plan with:
  trigger_type: "market_condition"
  trigger_condition: '{"regime": "volatile", "symbol": "BTCUSDT"}'
  planned_action: '{"type": "reduce_size", "factor": 0.5}'
  reasoning: "Historical data shows BreakoutEntry underperforms in volatile BTC regimes"

check_active_plans

Check if any active plans match current market conditions.

**Parameters:**

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | context_regime | string | No | Current market regime |

Agent Behavior

1. **After every Binance spot trade execution**, call `remember_trade` with full