Memory and ML Tools

Memory and ML tools give agents persistent memory, self-awareness of behavioral patterns, and predictive analytics on their own trading.

store_memory

Cost: $0.003 | Store a memory entry for later retrieval.

Agents can store observations, lessons, trade notes, or any structured data that they want to recall in future sessions. Memories are encrypted at rest using per-agent AES-256 keys.

Parameter	Type	Required	Description
content	string	Yes	The memory content to store.
category	string	No	Category tag (e.g. "lesson", "observation", "trade_note").
metadata	object	No	Key-value metadata for filtering.

result = client.call_tool(
    "store_memory",
    content="AAPL tends to gap down on earnings misses. Last 3 earnings: 2 gaps down, 1 flat.",
    category="observation",
    metadata={"symbol": "AAPL", "topic": "earnings"},
)

Returns: memory_id, stored_at, category.

---

search_memory

Cost: $0.003 | Search stored memories using semantic similarity.

Parameter	Type	Required	Description
query	string	Yes	Natural language search query.
category	string	No	Filter by category.
limit	int	No	Max results (default 10).

result = client.call_tool(
    "search_memory",
    query="What do I know about AAPL earnings behavior?",
    category="observation",
    limit=5,
)

Returns: memories[] each with memory_id, content, category, similarity_score, stored_at.

---

get_trading_biases

Cost: $0.005 | Analyze the agent’s trade history for behavioral biases.

Detects common biases: disposition effect (holding losers too long, cutting winners too short), recency bias, overtrading, loss aversion, anchoring, and confirmation bias.

Parameter	Type	Required	Description
trades	object[]	Yes	Recent trades with r_multiple, hold_time_hours, direction, symbol.
lookback_count	int	No	Number of trades to analyze (default 50).

result = client.call_tool(
    "get_trading_biases",
    trades=[
        {"r_multiple": "1.2", "hold_time_hours": "4", "direction": "long", "symbol": "AAPL"},
        {"r_multiple": "-0.5", "hold_time_hours": "48", "direction": "long", "symbol": "MSFT"},
        {"r_multiple": "0.3", "hold_time_hours": "2", "direction": "short", "symbol": "TSLA"},
    ],
)

Returns: biases[] each with bias_type, severity (LOW, MEDIUM, HIGH), evidence, recommendation.

---

get_behavioral_fingerprint

Cost: $0.005 | Generate a behavioral fingerprint of the agent’s trading style.

Characterizes the agent across multiple dimensions: risk appetite, hold duration profile, directional preference, asset concentration, time-of-day patterns, and streak behavior.

Parameter	Type	Required	Description
trades	object[]	Yes	Trade history with r_multiple, hold_time_hours, direction, symbol, entry_time.

result = client.call_tool(
    "get_behavioral_fingerprint",
    trades=recent_trades,
)

Returns: risk_profile (CONSERVATIVE, MODERATE, AGGRESSIVE), avg_hold_hours, directional_bias, concentration_score, streak_tendency, style_summary.

---

predict_trajectory

Cost: $0.008 | Predict the agent’s performance trajectory based on current patterns.

Uses recent R-multiples and behavioral data to forecast likely outcomes over the next N trades.

Parameter	Type	Required	Description
r_multiples	string[]	Yes	Recent R-multiples (minimum 10 recommended).
forecast_trades	int	No	Number of trades to forecast (default 20).
starting_equity	string	No	Starting equity for dollar projections.

result = client.call_tool(
    "predict_trajectory",
    r_multiples=["1.5", "-1.0", "2.3", "-0.5", "1.8", "-1.0", "3.2", "0.8", "1.1", "-0.7"],
    forecast_trades=50,
    starting_equity="100000",
)

Returns: projected_g, projected_equity, confidence_interval, trend_direction, risk_factors, recommendation.

---

detect_anomalies

Cost: $0.006 | Detect anomalous trades in the agent’s history.

Identifies trades that deviate significantly from the agent’s normal pattern. Useful for catching errors, unusual market conditions, or strategy drift.

Parameter	Type	Required	Description
r_multiples	string[]	Yes	R-multiples from trade history.
sensitivity	string	No	Detection sensitivity: "low", "medium", "high". Default "medium".

result = client.call_tool(
    "detect_anomalies",
    r_multiples=["1.5", "-1.0", "2.3", "-0.5", "1.8", "-1.0", "3.2", "0.8", "-4.5", "1.1"],
    sensitivity="medium",
)

Returns: anomalies[] each with index, r_multiple, z_score, anomaly_type (EXTREME_WIN, EXTREME_LOSS, PATTERN_BREAK), severity.

---

cluster_trades

Cost: $0.006 | Cluster trades by outcome pattern to discover hidden groupings.

Groups trades into clusters based on R-multiple, hold time, and other features. Reveals whether the agent has distinct “modes” of trading (e.g., a high-conviction mode vs. a scalping mode).

Parameter	Type	Required	Description
trades	object[]	Yes	Trades with r_multiple, hold_time_hours, and optional symbol, direction.
num_clusters	int	No	Number of clusters (default: auto-detect).

result = client.call_tool(
    "cluster_trades",
    trades=[
        {"r_multiple": "1.5", "hold_time_hours": "4", "symbol": "AAPL", "direction": "long"},
        {"r_multiple": "-0.3", "hold_time_hours": "0.5", "symbol": "TSLA", "direction": "short"},
        {"r_multiple": "2.8", "hold_time_hours": "72", "symbol": "MSFT", "direction": "long"},
    ],
)

Returns: clusters[] each with cluster_id, trade_count, avg_r, avg_hold_hours, dominant_direction, dominant_symbols[], g_contribution.