tradememory-bridge - SKILL.md Agent Skill

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 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:

Store the trade using remember_trade MCP tool
Before next trade, recall similar past trades using recall_memories MCP tool
Check agent state using get_agent_state to see if drawdown or confidence suggests pausing
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

After every Binance spot trade execution, call remember_trade with full context. Include market conditions, not just price data.
Before entering a new position, call recall_memories to check what happened in similar past conditions.
At session start, call get_agent_state to check if drawdown or losing streaks suggest reducing size.
Periodically, call get_behavioral_analysis to detect emerging biases.
Never skip journaling. Memory quality depends on consistent recording.
Use natural language for market_context. The richer the description, the better the recall matching.

Supported Exchanges

TradeMemory Protocol is exchange-agnostic. While this skill documents the Binance bridge workflow, the same MCP tools work with any trading data source — just pass the correct symbol format for your exchange.

Notes

All timestamps are UTC (ISO 8601 format).
pnl_r (R-multiple) is optional but significantly improves recall quality.
The context_regime field enables regime-filtered recall — strongly recommended.
TradeMemory stores data locally by default (SQLite). No data is sent to external servers unless you configure a hosted endpoint.
All 10 MCP tools are free and open source under MIT license.