data fixes; indicator=>workspace sync

2026-03-31 20:29:12 -04:00
parent 998f69fa1a
commit cd28e18e52
45 changed files with 1324 additions and 1239 deletions
--- a/gateway/src/harness/agent-harness.ts
+++ b/gateway/src/harness/agent-harness.ts
@@ -8,8 +8,9 @@ import type { InboundMessage, OutboundMessage } from '../types/messages.js';
 import { MCPClientConnector } from './mcp-client.js';
 import { LLMProviderFactory, type ProviderConfig } from '../llm/provider.js';
 import { ModelRouter, RoutingStrategy } from '../llm/router.js';
+import type { ModelMiddleware } from '../llm/middleware.js';
 import type { WorkspaceManager } from '../workspace/workspace-manager.js';
-import type { ChannelAdapter } from '../workspace/index.js';
+import type { ChannelAdapter, PathTriggerContext } from '../workspace/index.js';
 import type { ResearchSubagent } from './subagents/research/index.js';
 import type { DynamicStructuredTool } from '@langchain/core/tools';
 import { getToolRegistry } from '../tools/tool-registry.js';
@@ -70,10 +71,10 @@ export class AgentHarness {
  private config: AgentHarnessConfig;
  private modelFactory: LLMProviderFactory;
  private modelRouter: ModelRouter;
+  private middleware: ModelMiddleware | undefined;
  private mcpClient: MCPClientConnector;
  private workspaceManager?: WorkspaceManager;
  private channelAdapter?: ChannelAdapter;
-  private isFirstMessage: boolean = true;
  private researchSubagent?: ResearchSubagent;
  private availableMCPTools: MCPToolInfo[] = [];
  private researchImageCapture: Array<{ data: string; mimeType: string }> = [];
@@ -94,6 +95,8 @@ export class AgentHarness {
      mcpServerUrl: config.mcpServerUrl,
      logger: config.logger,
    });
+
+    this.registerWorkspaceTriggers();
  }

  /**
@@ -193,7 +196,7 @@ export class AgentHarness {
      const { createResearchSubagent } = await import('./subagents/research/index.js');

      // Create a model for the research subagent
-      const model = await this.modelRouter.route(
+      const { model } = await this.modelRouter.route(
        'research analysis', // dummy query
        this.config.license,
        RoutingStrategy.COMPLEXITY,
@@ -429,11 +432,25 @@ export class AgentHarness {

      // 2. Load recent conversation history
      const channelKey = this.config.channelType ?? ChannelType.WEBSOCKET;
-      const storedMessages = this.conversationStore
+      let storedMessages = this.conversationStore
        ? await this.conversationStore.getRecentMessages(
            this.config.userId, this.config.sessionId, this.config.historyLimit, channelKey
          )
        : [];
+
+      // First turn: seed conversation history with current workspace state
+      if (storedMessages.length === 0 && this.workspaceManager && this.conversationStore) {
+        const workspaceJSON = this.workspaceManager.serializeState();
+        const content = `[Workspace State]\n\`\`\`json\n${workspaceJSON}\n\`\`\``;
+        await this.conversationStore.saveMessage(
+          this.config.userId, this.config.sessionId,
+          'workspace', content, { isWorkspaceContext: true }, channelKey
+        );
+        storedMessages = await this.conversationStore.getRecentMessages(
+          this.config.userId, this.config.sessionId, this.config.historyLimit, channelKey
+        );
+      }
+
      const history = this.conversationStore
        ? this.conversationStore.toLangChainMessages(storedMessages)
        : [];
@@ -441,12 +458,13 @@ export class AgentHarness {

      // 4. Get the configured model
      this.config.logger.debug('Routing to model');
-      const model = await this.modelRouter.route(
+      const { model, middleware } = await this.modelRouter.route(
        message.content,
        this.config.license,
        RoutingStrategy.COMPLEXITY,
        this.config.userId
      );
+      this.middleware = middleware;
      this.config.logger.info({ modelName: model.constructor.name }, 'Model selected');

      // 5. Build LangChain messages
@@ -489,6 +507,11 @@ export class AgentHarness {
        'Tools loaded for main agent'
      );

+      // Apply middleware (e.g. Anthropic prompt caching)
+      const processedMessages = this.middleware
+        ? this.middleware.processMessages(langchainMessages, tools)
+        : langchainMessages;
+
      // 7. Bind tools to model
      const modelWithTools = tools.length > 0 && model.bindTools ? model.bindTools(tools) : model;

@@ -501,7 +524,7 @@ export class AgentHarness {

      // 8. Call LLM with tool calling loop
      this.config.logger.info('Invoking LLM with tool support');
-      const assistantMessage = await this.executeWithToolCalling(modelWithTools, langchainMessages, tools);
+      const assistantMessage = await this.executeWithToolCalling(modelWithTools, processedMessages, tools, 10);

      this.config.logger.info(
        { responseLength: assistantMessage.length },
@@ -518,11 +541,6 @@ export class AgentHarness {
        );
      }

-      // Mark first message as processed
-      if (this.isFirstMessage) {
-        this.isFirstMessage = false;
-      }
-
      return {
        messageId: `msg_${Date.now()}`,
        sessionId: message.sessionId,
@@ -556,16 +574,10 @@ export class AgentHarness {
  private async buildSystemPrompt(): Promise<string> {
    // Load template and populate with license info
    const template = await AgentHarness.loadSystemPromptTemplate();
-    let prompt = template
+    const prompt = template
      .replace('{{licenseType}}', this.config.license.licenseType)
      .replace('{{features}}', JSON.stringify(this.config.license.features, null, 2));

-    // Add full workspace state from WorkspaceManager (first message only)
-    if (this.isFirstMessage && this.workspaceManager) {
-      const workspaceJSON = this.workspaceManager.serializeState();
-      prompt += `\n\n# Current Workspace State\n\`\`\`json\n${workspaceJSON}\n\`\`\``;
-    }
-
    return prompt;
  }

@@ -574,9 +586,14 @@ export class AgentHarness {
   */
  private getToolLabel(toolName: string): string {
    const labels: Record<string, string> = {
-      research_agent: 'Researching...',
+      research: 'Researching...',
      get_chart_data: 'Fetching chart data...',
      symbol_lookup: 'Looking up symbol...',
+      category_list: 'Seeing what we have...',
+      category_edit: 'Coding...',
+      category_write: 'Coding...',
+      category_read: 'Inspecting...',
+      execute_research: 'Running script...',
    };
    return labels[toolName] ?? `Running ${toolName}...`;
  }
@@ -685,6 +702,26 @@ export class AgentHarness {
    return result;
  }

+  /**
+   * Register workspace path triggers to record state changes into conversation history.
+   */
+  private registerWorkspaceTriggers(): void {
+    if (!this.workspaceManager || !this.conversationStore) return;
+    const channelKey = this.config.channelType ?? ChannelType.WEBSOCKET;
+
+    for (const store of ['shapes', 'indicators', 'chartState']) {
+      this.workspaceManager.onPathChange(`/${store}/*`, async (_old: unknown, newVal: unknown, ctx: PathTriggerContext) => {
+        const content = `[Workspace Update] ${ctx.store}${ctx.path}\n${JSON.stringify(newVal, null, 2)}`;
+        await this.conversationStore!.saveMessage(
+          this.config.userId, this.config.sessionId,
+          'workspace', content,
+          { isWorkspaceUpdate: true, store: ctx.store, seq: ctx.seq },
+          channelKey
+        );
+      });
+    }
+  }
+
  /**
   * End the session: flush conversation to cold storage, then release resources.
   * Called by channel handlers on disconnect, session expiry, or graceful shutdown.
--- a/gateway/src/harness/memory/conversation-store.ts
+++ b/gateway/src/harness/memory/conversation-store.ts
@@ -11,7 +11,7 @@ export interface StoredMessage {
  id: string;
  userId: string;
  sessionId: string;
-  role: 'user' | 'assistant' | 'system';
+  role: 'user' | 'assistant' | 'system' | 'workspace';
  content: string;
  timestamp: number; // microseconds (Iceberg convention)
  metadata?: Record<string, unknown>;
@@ -44,7 +44,7 @@ export class ConversationStore {
  async saveMessage(
    userId: string,
    sessionId: string,
-    role: 'user' | 'assistant' | 'system',
+    role: 'user' | 'assistant' | 'system' | 'workspace',
    content: string,
    metadata?: Record<string, unknown>,
    channelType?: string
@@ -171,6 +171,8 @@ export class ConversationStore {
          return new AIMessage(msg.content);
        case 'system':
          return new SystemMessage(msg.content);
+        case 'workspace':
+          return new HumanMessage(msg.content);
        default:
          throw new Error(`Unknown role: ${msg.role}`);
      }
--- a/gateway/src/harness/prompts/system-prompt.md
+++ b/gateway/src/harness/prompts/system-prompt.md
@@ -59,6 +59,11 @@ Example usage:
 - User: "Does Friday price action correlate with Monday?"
 - You: Call research tool with instruction="Analyze correlation between Friday and Monday price action during NY trading hours (9:30-4:00 ET)", name="Friday-Monday Correlation"

+### category_list
+List existing research scripts (category="research").
+Use this before calling the research tool to check whether a relevant script already exists.
+If one does, pass its exact name to the research tool so the subagent updates it rather than creating a new one.
+
 ### symbol-lookup
 Look up trading symbols and get metadata.
 Use this when users mention tickers or need symbol information.
--- a/gateway/src/harness/subagents/research/config.yaml
+++ b/gateway/src/harness/subagents/research/config.yaml
@@ -12,6 +12,7 @@ maxTokens: 8192
 memoryFiles:
  - api-reference.md
  - usage-examples.md
+  - pandas-ta-reference.md

 # System prompt file
 systemPromptFile: system-prompt.md
--- a/gateway/src/harness/subagents/research/memory/pandas-ta-reference.md
+++ b/gateway/src/harness/subagents/research/memory/pandas-ta-reference.md
@@ -0,0 +1,227 @@
+# pandas-ta Reference for Research Scripts
+
+The sandbox environment uses **pandas-ta** as the standard indicator library. Always use it for technical indicator calculations; do not write manual rolling/ewm implementations.
+
+```python
+import pandas_ta as ta
+```
+
+## Calling Convention
+
+pandas-ta functions accept a Series (or OHLCV columns) plus keyword parameters that match pandas-ta's documented argument names:
+
+```python
+# Single-series indicator
+rsi = ta.rsi(df['close'], length=14)          # returns Series
+
+# OHLCV indicator
+atr = ta.atr(df['high'], df['low'], df['close'], length=14)
+
+# Multi-output indicator (returns DataFrame)
+macd_df = ta.macd(df['close'], fast=12, slow=26, signal=9)
+# columns: MACD_12_26_9, MACDh_12_26_9, MACDs_12_26_9
+
+bbands_df = ta.bbands(df['close'], length=20, std=2.0)
+# columns: BBL_20_2.0, BBM_20_2.0, BBU_20_2.0, BBB_20_2.0, BBP_20_2.0
+```
+
+## Default Parameters
+
+Key defaults to keep in mind:
+- Most period/length indicators: `length=14` (use `length=` not `timeperiod=`)
+- `bbands`: `length=20, std=2.0` (note: single `std`, not separate upper/lower)
+- `macd`: `fast=12, slow=26, signal=9`
+- `stoch`: `k=14, d=3, smooth_k=3`
+- `psar`: `af0=0.02, af=0.02, max_af=0.2`
+- `vwap`: `anchor='D'` (requires DatetimeIndex)
+- `ichimoku`: `tenkan=9, kijun=26, senkou=52`
+
+## Available Indicators
+
+These match the indicators supported by the TradingView web client. Use the pandas-ta function name shown here (lowercase):
+
+### Overlap / Moving Averages — plotted on the price pane
+
+| Function | Description |
+|----------|-------------|
+| `sma` | Simple Moving Average — plain arithmetic mean over `length` periods |
+| `ema` | Exponential Moving Average — more weight on recent prices |
+| `wma` | Weighted Moving Average — linearly increasing weights |
+| `dema` | Double EMA — two layers of EMA to reduce lag |
+| `tema` | Triple EMA — three layers of EMA, even less lag than DEMA |
+| `trima` | Triangular MA — double-smoothed SMA, very smooth |
+| `kama` | Kaufman Adaptive MA — adapts speed to market noise/trending conditions |
+| `t3` | T3 Moving Average — Tillson's smooth, low-lag MA using six EMAs |
+| `hma` | Hull MA — very low-lag MA using WMAs |
+| `alma` | Arnaud Legoux MA — Gaussian-weighted MA with reduced lag and noise |
+| `midpoint` | Midpoint of close over `length` periods: (highest + lowest) / 2 |
+| `midprice` | Midpoint of high/low over `length` periods |
+| `supertrend` | Trend-following band (ATR-based) that flips above/below price |
+| `ichimoku` | Ichimoku Cloud — multi-line Japanese trend/support/resistance system |
+| `vwap` | Volume-Weighted Average Price — average price weighted by volume, resets on `anchor` |
+| `vwma` | Volume-Weighted MA — like SMA but candles weighted by volume |
+| `bbands` | Bollinger Bands — SMA ± N standard deviations; returns upper, mid, lower bands |
+
+### Momentum — typically plotted in a separate pane
+
+| Function | Description |
+|----------|-------------|
+| `rsi` | Relative Strength Index — 0–100 oscillator measuring speed of price changes |
+| `macd` | MACD — difference of two EMAs plus signal line and histogram |
+| `stoch` | Stochastic Oscillator — %K/%D, measures close vs recent high/low range |
+| `stochrsi` | Stochastic RSI — applies stochastic formula to RSI values |
+| `cci` | Commodity Channel Index — deviation of price from its statistical mean |
+| `willr` | Williams %R — inverse stochastic, −100 to 0 oscillator |
+| `mom` | Momentum — raw price change over `length` periods |
+| `roc` | Rate of Change — percentage price change over `length` periods |
+| `trix` | TRIX — 1-period % change of a triple-smoothed EMA |
+| `cmo` | Chande Momentum Oscillator — ratio of up/down momentum, −100 to 100 |
+| `adx` | Average Directional Index — strength of trend (0–100, direction-agnostic) |
+| `aroon` | Aroon — measures how recently the highest/lowest price occurred; returns Up, Down, Oscillator |
+| `ao` | Awesome Oscillator — difference of 5- and 34-period simple MAs of midprice |
+| `bop` | Balance of Power — measures buying vs selling pressure: (close−open)/(high−low) |
+| `uo` | Ultimate Oscillator — weighted combo of three period (fast/medium/slow) buying pressure ratios |
+| `apo` | Absolute Price Oscillator — difference between two EMAs (like MACD without signal line) |
+| `mfi` | Money Flow Index — RSI-like oscillator using price × volume |
+| `coppock` | Coppock Curve — long-term momentum oscillator based on rate-of-change |
+| `dpo` | Detrended Price Oscillator — removes trend to show cycle oscillations |
+| `fisher` | Fisher Transform — converts price into a Gaussian normal distribution |
+| `rvgi` | Relative Vigor Index — compares close−open to high−low to measure trend vigor |
+| `kst` | Know Sure Thing — momentum oscillator from four ROC periods, smoothed |
+
+### Volatility — plotted on price pane or separate
+
+| Function | Description |
+|----------|-------------|
+| `atr` | Average True Range — average of true range (greatest of H−L, H−prevC, L−prevC) |
+| `kc` | Keltner Channels — EMA ± N × ATR bands around price |
+| `donchian` | Donchian Channels — highest high / lowest low over `length` periods |
+
+### Volume — plotted in separate pane
+
+| Function | Description |
+|----------|-------------|
+| `obv` | On Balance Volume — cumulative volume, added on up days, subtracted on down days |
+| `ad` | Accumulation/Distribution — running total of the money flow multiplier × volume |
+| `adosc` | Chaikin Oscillator — EMA difference of the A/D line |
+| `cmf` | Chaikin Money Flow — sum of (money flow volume) / sum of volume over `length` |
+| `eom` | Ease of Movement — relates price change to volume; high = price moves easily |
+| `efi` | Elder's Force Index — combines price change direction with volume magnitude |
+| `kvo` | Klinger Volume Oscillator — EMA difference of volume force |
+| `pvt` | Price Volume Trend — cumulative: volume × percentage price change |
+
+### Statistics / Price Transforms
+
+| Function | Description |
+|----------|-------------|
+| `stdev` | Standard Deviation of close over `length` periods |
+| `linreg` | Linear Regression Curve — least-squares line endpoint value over `length` periods |
+| `slope` | Linear Regression Slope — gradient of the regression line |
+| `hl2` | Median Price — (high + low) / 2 |
+| `hlc3` | Typical Price — (high + low + close) / 3 |
+| `ohlc4` | Average Price — (open + high + low + close) / 4 |
+
+### Trend
+
+| Function | Description |
+|----------|-------------|
+| `psar` | Parabolic SAR — trailing stop-and-reverse dots that follow price |
+| `vortex` | Vortex Indicator — VI+ / VI− lines measuring upward vs downward trend movement |
+| `chop` | Choppiness Index — 0–100, high = choppy/sideways, low = strong trend |
+
+## Usage Examples
+
+### Single-output indicators
+
+```python
+import pandas_ta as ta
+
+df['rsi'] = ta.rsi(df['close'], length=14)
+df['ema_20'] = ta.ema(df['close'], length=20)
+df['sma_50'] = ta.sma(df['close'], length=50)
+df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)
+df['obv'] = ta.obv(df['close'], df['volume'])
+df['adx'] = ta.adx(df['high'], df['low'], df['close'], length=14)['ADX_14']
+```
+
+### Multi-output indicators — extract columns by position
+
+```python
+# MACD → MACD_12_26_9, MACDh_12_26_9, MACDs_12_26_9
+macd_df = ta.macd(df['close'], fast=12, slow=26, signal=9)
+df['macd']        = macd_df.iloc[:, 0]  # MACD line
+df['macd_hist']   = macd_df.iloc[:, 1]  # Histogram
+df['macd_signal'] = macd_df.iloc[:, 2]  # Signal line
+
+# Bollinger Bands → BBL, BBM, BBU, BBB, BBP
+bb_df = ta.bbands(df['close'], length=20, std=2.0)
+df['bb_lower'] = bb_df.iloc[:, 0]  # BBL
+df['bb_mid']   = bb_df.iloc[:, 1]  # BBM
+df['bb_upper'] = bb_df.iloc[:, 2]  # BBU
+
+# Stochastic → STOCHk, STOCHd
+stoch_df = ta.stoch(df['high'], df['low'], df['close'], k=14, d=3, smooth_k=3)
+df['stoch_k'] = stoch_df.iloc[:, 0]
+df['stoch_d'] = stoch_df.iloc[:, 1]
+
+# Keltner Channels → KCLe, KCBe, KCUe
+kc_df = ta.kc(df['high'], df['low'], df['close'], length=20)
+df['kc_lower'] = kc_df.iloc[:, 0]
+df['kc_mid']   = kc_df.iloc[:, 1]
+df['kc_upper'] = kc_df.iloc[:, 2]
+
+# ADX → ADX_14, DMP_14, DMN_14
+adx_df = ta.adx(df['high'], df['low'], df['close'], length=14)
+df['adx']  = adx_df.iloc[:, 0]  # ADX strength
+df['dmp']  = adx_df.iloc[:, 1]  # +DI
+df['dmn']  = adx_df.iloc[:, 2]  # -DI
+
+# Aroon → AROOND_14, AROONU_14, AROONOSC_14
+aroon_df = ta.aroon(df['high'], df['low'], length=14)
+df['aroon_down'] = aroon_df.iloc[:, 0]
+df['aroon_up']   = aroon_df.iloc[:, 1]
+
+# Donchian Channels → DCL, DCM, DCU
+dc_df = ta.donchian(df['high'], df['low'], lower_length=20, upper_length=20)
+df['dc_lower'] = dc_df.iloc[:, 0]
+df['dc_mid']   = dc_df.iloc[:, 1]
+df['dc_upper'] = dc_df.iloc[:, 2]
+```
+
+### Charting with indicators
+
+```python
+import pandas_ta as ta
+from dexorder.api import get_api
+import asyncio
+
+api = get_api()
+
+df = asyncio.run(api.data.historical_ohlc(
+    ticker="BINANCE:BTC/USDT",
+    period_seconds=3600,
+    start_time="2024-01-01",
+    end_time="2024-01-08",
+    extra_columns=["volume"]
+))
+
+# Compute indicators
+df['ema_20'] = ta.ema(df['close'], length=20)
+df['rsi'] = ta.rsi(df['close'], length=14)
+macd_df = ta.macd(df['close'])
+df['macd'] = macd_df.iloc[:, 0]
+df['macd_signal'] = macd_df.iloc[:, 2]
+
+# Main price chart with EMA overlay
+fig, ax = api.charting.plot_ohlc(df, title="BTC/USDT 1H", volume=True)
+ax.plot(df.index, df['ema_20'], label="EMA 20", color="orange", linewidth=1.5)
+ax.legend()
+
+# RSI panel
+rsi_ax = api.charting.add_indicator_panel(fig, df, columns=["rsi"], ylabel="RSI", ylim=(0, 100))
+rsi_ax.axhline(70, color='red', linestyle='--', alpha=0.5)
+rsi_ax.axhline(30, color='green', linestyle='--', alpha=0.5)
+
+# MACD panel
+api.charting.add_indicator_panel(fig, df, columns=["macd", "macd_signal"], ylabel="MACD")
+```
--- a/gateway/src/harness/subagents/research/memory/usage-examples.md
+++ b/gateway/src/harness/subagents/research/memory/usage-examples.md
@@ -112,10 +112,12 @@ fig, ax = api.charting.plot_ohlc(

 ### Adding Indicator Panels

+Use **pandas-ta** for all indicator calculations. Do not write manual rolling/ewm implementations.
+
 ```python
 from dexorder.api import get_api
 import asyncio
-import pandas as pd
+import pandas_ta as ta

 api = get_api()

@@ -127,8 +129,9 @@ df = asyncio.run(api.data.historical_ohlc(
    end_time="2021-12-21"
 ))

-# Calculate a simple moving average
-df['sma_20'] = df['close'].rolling(window=20).mean()
+# Calculate indicators using pandas-ta
+df['sma_20'] = ta.sma(df['close'], length=20)
+df['rsi'] = ta.rsi(df['close'], length=14)

 # Create chart
 fig, ax = api.charting.plot_ohlc(df, title="BTC/USDT with SMA")
@@ -138,7 +141,6 @@ ax.plot(df.index, df['sma_20'], label="SMA 20", color="blue", linewidth=2)
 ax.legend()

 # Add RSI indicator panel below
-df['rsi'] = calculate_rsi(df['close'], 14)  # Your RSI calculation
 rsi_ax = api.charting.add_indicator_panel(
    fig, df,
    columns=["rsi"],
@@ -149,12 +151,40 @@ rsi_ax.axhline(70, color='red', linestyle='--', alpha=0.5)
 rsi_ax.axhline(30, color='green', linestyle='--', alpha=0.5)
 ```

+### Multi-Output Indicators
+
+Some pandas-ta indicators return a DataFrame. Extract the columns you need:
+
+```python
+import pandas_ta as ta
+
+# MACD returns: MACD_12_26_9, MACDh_12_26_9, MACDs_12_26_9
+macd_df = ta.macd(df['close'], fast=12, slow=26, signal=9)
+df['macd'] = macd_df.iloc[:, 0]      # MACD line
+df['macd_hist'] = macd_df.iloc[:, 1] # Histogram
+df['macd_signal'] = macd_df.iloc[:, 2] # Signal line
+
+# Bollinger Bands returns: BBL, BBM, BBU, BBB, BBP
+bb_df = ta.bbands(df['close'], length=20, std=2.0)
+df['bb_upper'] = bb_df.iloc[:, 2]  # BBU
+df['bb_mid']   = bb_df.iloc[:, 1]  # BBM
+df['bb_lower'] = bb_df.iloc[:, 0]  # BBL
+
+# Stochastic returns: STOCHk, STOCHd
+stoch_df = ta.stoch(df['high'], df['low'], df['close'], k=14, d=3, smooth_k=3)
+df['stoch_k'] = stoch_df.iloc[:, 0]
+df['stoch_d'] = stoch_df.iloc[:, 1]
+
+# ATR (uses high, low, close)
+df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)
+```
+
 ## Complete Example

 ```python
 from dexorder.api import get_api
 import asyncio
-import pandas as pd
+import pandas_ta as ta

 # Get API instance
 api = get_api()
@@ -168,9 +198,9 @@ df = asyncio.run(api.data.historical_ohlc(
    extra_columns=["volume"]
 ))

-# Add some analysis
-df['sma_20'] = df['close'].rolling(window=20).mean()
-df['sma_50'] = df['close'].rolling(window=50).mean()
+# Add moving averages using pandas-ta
+df['sma_20'] = ta.sma(df['close'], length=20)
+df['ema_50'] = ta.ema(df['close'], length=50)

 # Create chart with volume
 fig, ax = api.charting.plot_ohlc(
@@ -182,7 +212,7 @@ fig, ax = api.charting.plot_ohlc(

 # Overlay moving averages
 ax.plot(df.index, df['sma_20'], label="SMA 20", color="blue", linewidth=1.5)
-ax.plot(df.index, df['sma_50'], label="SMA 50", color="red", linewidth=1.5)
+ax.plot(df.index, df['ema_50'], label="EMA 50", color="red", linewidth=1.5)
 ax.legend()

 # Print summary statistics
--- a/gateway/src/harness/subagents/research/system-prompt.md
+++ b/gateway/src/harness/subagents/research/system-prompt.md
@@ -51,7 +51,140 @@ The API provides two main components:
 - `api.data` - DataAPI for fetching OHLC market data
 - `api.charting` - ChartingAPI for creating financial charts

-See your knowledge base for complete API documentation and examples.
+See your knowledge base for complete API documentation, examples, and the full pandas-ta indicator reference (see `pandas-ta-reference.md`).
+
+## Technical Indicators — pandas-ta
+
+The sandbox environment uses **pandas-ta** as the standard indicator library. Always use it for technical indicator calculations; do not write manual rolling/ewm implementations.
+
+```python
+import pandas_ta as ta
+```
+
+### Calling Convention
+
+pandas-ta functions accept a Series (or OHLCV columns) plus keyword parameters that match pandas-ta's documented argument names:
+
+```python
+# Single-series indicator
+rsi = ta.rsi(df['close'], length=14)          # returns Series
+
+# OHLCV indicator
+atr = ta.atr(df['high'], df['low'], df['close'], length=14)
+
+# Multi-output indicator (returns DataFrame)
+macd_df = ta.macd(df['close'], fast=12, slow=26, signal=9)
+# columns: MACD_12_26_9, MACDh_12_26_9, MACDs_12_26_9
+
+bbands_df = ta.bbands(df['close'], length=20, std=2.0)
+# columns: BBL_20_2.0, BBM_20_2.0, BBU_20_2.0, BBB_20_2.0, BBP_20_2.0
+```
+
+### Available Indicators (canonical list)
+
+These match the indicators supported by the TradingView web client. Use the pandas-ta function name shown here (lowercase):
+
+**Overlap / Moving Averages** — plotted on the price pane
+
+| Function | Description |
+|----------|-------------|
+| `sma` | Simple Moving Average — plain arithmetic mean over `length` periods |
+| `ema` | Exponential Moving Average — more weight on recent prices |
+| `wma` | Weighted Moving Average — linearly increasing weights |
+| `dema` | Double EMA — two layers of EMA to reduce lag |
+| `tema` | Triple EMA — three layers of EMA, even less lag than DEMA |
+| `trima` | Triangular MA — double-smoothed SMA, very smooth |
+| `kama` | Kaufman Adaptive MA — adapts speed to market noise/trending conditions |
+| `t3` | T3 Moving Average — Tillson's smooth, low-lag MA using six EMAs |
+| `hma` | Hull MA — very low-lag MA using WMAs |
+| `alma` | Arnaud Legoux MA — Gaussian-weighted MA with reduced lag and noise |
+| `midpoint` | Midpoint of close over `length` periods: (highest + lowest) / 2 |
+| `midprice` | Midpoint of high/low over `length` periods |
+| `supertrend` | Trend-following band (ATR-based) that flips above/below price |
+| `ichimoku` | Ichimoku Cloud — multi-line Japanese trend/support/resistance system |
+| `vwap` | Volume-Weighted Average Price — average price weighted by volume, resets on `anchor` |
+| `vwma` | Volume-Weighted MA — like SMA but candles weighted by volume |
+| `bbands` | Bollinger Bands — SMA ± N standard deviations; returns upper, mid, lower bands |
+
+**Momentum** — typically plotted in a separate pane
+
+| Function | Description |
+|----------|-------------|
+| `rsi` | Relative Strength Index — 0–100 oscillator measuring speed of price changes |
+| `macd` | MACD — difference of two EMAs plus signal line and histogram |
+| `stoch` | Stochastic Oscillator — %K/%D, measures close vs recent high/low range |
+| `stochrsi` | Stochastic RSI — applies stochastic formula to RSI values |
+| `cci` | Commodity Channel Index — deviation of price from its statistical mean |
+| `willr` | Williams %R — inverse stochastic, −100 to 0 oscillator |
+| `mom` | Momentum — raw price change over `length` periods |
+| `roc` | Rate of Change — percentage price change over `length` periods |
+| `trix` | TRIX — 1-period % change of a triple-smoothed EMA |
+| `cmo` | Chande Momentum Oscillator — ratio of up/down momentum, −100 to 100 |
+| `adx` | Average Directional Index — strength of trend (0–100, direction-agnostic) |
+| `aroon` | Aroon — measures how recently the highest/lowest price occurred; returns Up, Down, Oscillator |
+| `ao` | Awesome Oscillator — difference of 5- and 34-period simple MAs of midprice |
+| `bop` | Balance of Power — measures buying vs selling pressure: (close−open)/(high−low) |
+| `uo` | Ultimate Oscillator — weighted combo of three period (fast/medium/slow) buying pressure ratios |
+| `apo` | Absolute Price Oscillator — difference between two EMAs (like MACD without signal line) |
+| `mfi` | Money Flow Index — RSI-like oscillator using price × volume |
+| `coppock` | Coppock Curve — long-term momentum oscillator based on rate-of-change |
+| `dpo` | Detrended Price Oscillator — removes trend to show cycle oscillations |
+| `fisher` | Fisher Transform — converts price into a Gaussian normal distribution |
+| `rvgi` | Relative Vigor Index — compares close−open to high−low to measure trend vigor |
+| `kst` | Know Sure Thing — momentum oscillator from four ROC periods, smoothed |
+
+**Volatility** — plotted on price pane or separate
+
+| Function | Description |
+|----------|-------------|
+| `atr` | Average True Range — average of true range (greatest of H−L, H−prevC, L−prevC) |
+| `kc` | Keltner Channels — EMA ± N × ATR bands around price |
+| `donchian` | Donchian Channels — highest high / lowest low over `length` periods |
+
+**Volume** — plotted in separate pane
+
+| Function | Description |
+|----------|-------------|
+| `obv` | On Balance Volume — cumulative volume, added on up days, subtracted on down days |
+| `ad` | Accumulation/Distribution — running total of the money flow multiplier × volume |
+| `adosc` | Chaikin Oscillator — EMA difference of the A/D line |
+| `cmf` | Chaikin Money Flow — sum of (money flow volume) / sum of volume over `length` |
+| `eom` | Ease of Movement — relates price change to volume; high = price moves easily |
+| `efi` | Elder's Force Index — combines price change direction with volume magnitude |
+| `kvo` | Klinger Volume Oscillator — EMA difference of volume force |
+| `pvt` | Price Volume Trend — cumulative: volume × percentage price change |
+
+**Statistics / Price Transforms**
+
+| Function | Description |
+|----------|-------------|
+| `stdev` | Standard Deviation of close over `length` periods |
+| `linreg` | Linear Regression Curve — least-squares line endpoint value over `length` periods |
+| `slope` | Linear Regression Slope — gradient of the regression line |
+| `hl2` | Median Price — (high + low) / 2 |
+| `hlc3` | Typical Price — (high + low + close) / 3 |
+| `ohlc4` | Average Price — (open + high + low + close) / 4 |
+
+**Trend**
+
+| Function | Description |
+|----------|-------------|
+| `psar` | Parabolic SAR — trailing stop-and-reverse dots that follow price |
+| `vortex` | Vortex Indicator — VI+ / VI− lines measuring upward vs downward trend movement |
+| `chop` | Choppiness Index — 0–100, high = choppy/sideways, low = strong trend |
+
+### Default Parameters
+
+Key defaults to keep in mind:
+- Most period/length indicators: `length=14` (use `length=` not `timeperiod=`)
+- `bbands`: `length=20, std=2.0` (note: single `std`, not separate upper/lower)
+- `macd`: `fast=12, slow=26, signal=9`
+- `stoch`: `k=14, d=3, smooth_k=3`
+- `psar`: `af0=0.02, af=0.02, max_af=0.2`
+- `vwap`: `anchor='D'` (requires DatetimeIndex)
+- `ichimoku`: `tenkan=9, kijun=26, senkou=52`
+
+For multi-output indicator column extraction patterns and complete charting examples, fetch `pandas-ta-reference.md` from your knowledge base.

 ## Coding Loop Pattern

@@ -59,11 +192,9 @@ When a user requests analysis:

 1. **Understand the request**: What data is needed? What analysis? What visualization?

-2. **Check for existing scripts**: Use `category_list` to see if a similar script exists
-   - If exists and suitable: use `category_read` to review it
-   - Consider editing existing script vs creating new one
+2. **Use the provided name**: The instruction will begin with `Research script name: "<name>"`. Always use that exact name when calling `category_write` or `category_edit`. Check first with `category_read` — if the script already exists, use `category_edit` to update it rather than creating a new one with `category_write`.

-3. **Write the script**: Use `category_write` (or `category_edit`)
+3. **Write the script**: Use `category_write` (new) or `category_edit` (existing)
   - Write clean, well-commented Python code
   - Include proper error handling
   - Use appropriate ticker symbols, time ranges, and periods
@@ -106,10 +237,7 @@ When a user requests analysis:
  - Add `conda_packages: ["package-name"]` to metadata
  - Packages are auto-installed during validation

- **Script naming**: Choose descriptive, unique names. Examples:
-  - "BTC Weekly Analysis"
-  - "ETH Volume Profile"
-  - "Market Correlation Heatmap"
+- **Script naming**: Always use the name provided in the instruction (`Research script name: "<name>"`). Do not invent a different name.

 - **Error handling**: Wrap data fetching in try/except to provide helpful error messages