Tradememory Bridge

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:

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:

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:

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:

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:

create_trading_plan

Set conditional plans that trigger on specific market conditions.

Parameters:

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:

Agent Behavior

1. After every Binance spot trade execution, call remember_trade with full context. Include market conditions, not just price data.
2. Before entering a new position, call recall_memories to check what happened in similar past conditions.
3. At session start, call get_agent_state to check if drawdown or losing streaks suggest reducing size.
4. Periodically, call get_behavioral_analysis to detect emerging biases.
5. Never skip journaling. Memory quality depends on consistent recording.
6. 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

1. All timestamps are UTC (ISO 8601 format).
2. pnl_r (R-multiple) is optional but significantly improves recall quality.
3. The context_regime field enables regime-filtered recall — strongly recommended.
4. TradeMemory stores data locally by default (SQLite). No data is sent to external servers unless you configure a hosted endpoint.
5. All 17 MCP tools are free and open source under MIT license.