diff --git a/python/valuecell/agents/common/trading/_internal/coordinator.py b/python/valuecell/agents/common/trading/_internal/coordinator.py
index 0932c6493..606e1a809 100644
--- a/python/valuecell/agents/common/trading/_internal/coordinator.py
+++ b/python/valuecell/agents/common/trading/_internal/coordinator.py
@@ -91,7 +91,7 @@ def __init__(
self._request = request
self.strategy_id = strategy_id
self.portfolio_service = portfolio_service
- self._features_pipeline = features_pipeline
+ self.features_pipeline = features_pipeline
self._composer = composer
self._execution_gateway = execution_gateway
self._history_recorder = history_recorder
@@ -144,7 +144,7 @@ async def run_once(self) -> DecisionCycleResult:
if self._request.exchange_config.market_type == MarketType.SPOT:
portfolio.buying_power = max(0.0, float(portfolio.account_balance))
- pipeline_result = await self._features_pipeline.build()
+ pipeline_result = await self.features_pipeline.build()
features = list(pipeline_result.features or [])
market_features = extract_market_snapshot_features(features)
digest = self._digest_builder.build(self._history_recorder.get_records())
@@ -612,7 +612,7 @@ async def close_all_positions(self) -> List[TradeHistoryEntry]:
market_features: List[FeatureVector] = []
if self._request.exchange_config.trading_mode == TradingMode.VIRTUAL:
try:
- pipeline_result = await self._features_pipeline.build()
+ pipeline_result = await self.features_pipeline.build()
market_features = extract_market_snapshot_features(
pipeline_result.features or []
)
diff --git a/python/valuecell/agents/common/trading/base_agent.py b/python/valuecell/agents/common/trading/base_agent.py
index c1776e3b8..c59de2d1d 100644
--- a/python/valuecell/agents/common/trading/base_agent.py
+++ b/python/valuecell/agents/common/trading/base_agent.py
@@ -244,6 +244,15 @@ async def _run_background_decision(
# Call user hook for custom initialization
try:
+ # Initialize long-lived features resources if available
+ try:
+ await runtime.coordinator.features_pipeline.open()
+ except Exception:
+ logger.exception(
+ "Error initializing features pipeline resources for strategy {}",
+ strategy_id,
+ )
+
await self._on_start(runtime, request)
except Exception:
logger.exception("Error in _on_start hook for strategy {}", strategy_id)
@@ -336,6 +345,13 @@ async def _run_background_decision(
)
# Finalize: close resources and mark stopped/paused/error
+ try:
+ await runtime.coordinator.features_pipeline.close()
+ except Exception:
+ logger.exception(
+ "Error closing features pipeline resources for strategy {}",
+ strategy_id,
+ )
await controller.finalize(
runtime, reason=stop_reason, reason_detail=stop_reason_detail
)
diff --git a/python/valuecell/agents/common/trading/constants.py b/python/valuecell/agents/common/trading/constants.py
index 74f262fa9..6dda52bb6 100644
--- a/python/valuecell/agents/common/trading/constants.py
+++ b/python/valuecell/agents/common/trading/constants.py
@@ -15,3 +15,4 @@
FEATURE_GROUP_BY_KEY = "group_by_key"
FEATURE_GROUP_BY_INTERVAL_PREFIX = "interval_"
FEATURE_GROUP_BY_MARKET_SNAPSHOT = "market_snapshot"
+FEATURE_GROUP_BY_IMAGE_ANALYSIS = "image_analysis"
diff --git a/python/valuecell/agents/common/trading/data/configs/aggr/charts.json b/python/valuecell/agents/common/trading/data/configs/aggr/charts.json
new file mode 100644
index 000000000..ba06c4cee
--- /dev/null
+++ b/python/valuecell/agents/common/trading/data/configs/aggr/charts.json
@@ -0,0 +1,3150 @@
+{
+ "version": 7,
+ "createdAt": 1764666200000,
+ "updatedAt": 1764666205739,
+ "name": "Charts",
+ "id": "charts",
+ "states": {
+ "panes": {
+ "_id": "panes",
+ "locked": false,
+ "layout": [
+ {
+ "i": "chart",
+ "type": "chart",
+ "x": 0,
+ "y": 0,
+ "w": 24,
+ "h": 23,
+ "moved": false
+ },
+ {
+ "i": "delta",
+ "type": "chart",
+ "x": 0,
+ "y": 23,
+ "w": 24,
+ "h": 18,
+ "moved": false
+ },
+ {
+ "i": "chart copy 1",
+ "type": "chart",
+ "x": 0,
+ "y": 41,
+ "w": 24,
+ "h": 17,
+ "moved": false
+ }
+ ],
+ "panes": {
+ "chart": {
+ "id": "chart",
+ "name": "AGGR",
+ "type": "chart",
+ "markets": [
+ "BINANCE_FUTURES:btcusd_perp",
+ "BINANCE_FUTURES:btcusdc",
+ "BINANCE_FUTURES:btcusdt",
+ "BINANCE:btcfdusd",
+ "BINANCE:btctusd",
+ "BINANCE:btcusdc",
+ "BINANCE:btcusdt",
+ "BITFINEX:BTCF0:USTF0",
+ "BITFINEX:BTCUSD",
+ "BITFINEX:BTCUST",
+ "BITGET:BTCPERP_CMCBL",
+ "BITGET:BTCUSD_DMCBL",
+ "BITGET:BTCUSDC",
+ "BITGET:BTCUSDT",
+ "BITGET:BTCUSDT_UMCBL",
+ "BITMEX:XBT_USDT",
+ "BITMEX:XBTUSD",
+ "BITMEX:XBTUSDT",
+ "BITSTAMP:btcusd",
+ "BITSTAMP:btcusdc",
+ "BITSTAMP:btcusdt",
+ "BYBIT:BTCUSD",
+ "BYBIT:BTCUSDC-SPOT",
+ "BYBIT:BTCUSDT",
+ "BYBIT:BTCUSDT-SPOT",
+ "COINBASE:BTC-USD",
+ "COINBASE:BTC-USDC",
+ "COINBASE:BTC-USDT",
+ "DERIBIT:BTC_USDC-PERPETUAL",
+ "DERIBIT:BTC-PERPETUAL",
+ "DYDX:BTC-USD",
+ "HUOBI:BTC-USD",
+ "HUOBI:BTC-USDT",
+ "HUOBI:btcusdc",
+ "HUOBI:btcusdd",
+ "HUOBI:btcusdt",
+ "KRAKEN:PF_XBTUSD",
+ "KRAKEN:PI_XBTUSD",
+ "KRAKEN:XBT/USD",
+ "KRAKEN:XBT/USDC",
+ "KRAKEN:XBT/USDT",
+ "KUCOIN:BTC-USDC",
+ "KUCOIN:BTC-USDT",
+ "KUCOIN:XBTUSDCM",
+ "KUCOIN:XBTUSDM",
+ "KUCOIN:XBTUSDTM",
+ "MEXC:BTC_USD",
+ "MEXC:BTC_USDT",
+ "MEXC:BTCUSDC",
+ "MEXC:BTCUSDT",
+ "OKEX:BTC-USD-SWAP",
+ "OKEX:BTC-USDC",
+ "OKEX:BTC-USDC-SWAP",
+ "OKEX:BTC-USDT",
+ "OKEX:BTC-USDT-SWAP",
+ "PHEMEX:BTCUSD",
+ "PHEMEX:BTCUSDT",
+ "POLONIEX:BTC_TUSD",
+ "POLONIEX:BTC_USDC",
+ "POLONIEX:BTC_USDD",
+ "POLONIEX:BTC_USDT"
+ ],
+ "zoom": 1
+ },
+ "delta": {
+ "id": "delta",
+ "name": "PAIRS",
+ "type": "chart",
+ "zoom": 1,
+ "settings": {
+ "indicatorsErrors": {},
+ "indicators": {
+ "_rmg9m2zvgvdyfzgl": {
+ "enabled": true,
+ "name": "Liquidations",
+ "description": "Liquidations by side",
+ "script": "plothistogram(lbuy, color=options.upColor)\nplothistogram(-lsell, color=options.downColor)",
+ "options": {
+ "priceScaleId": "_rmg9m2zvgvdyfzgl",
+ "priceFormat": {
+ "type": "volume"
+ },
+ "upColor": "rgb(100,181,246)",
+ "downColor": "rgb(240,98,146)",
+ "scaleMargins": {
+ "top": 0.87,
+ "bottom": 0
+ },
+ "baseLineVisible": false,
+ "lastValueVisible": true,
+ "priceLineVisible": false
+ },
+ "id": "_rmg9m2zvgvdyfzgl",
+ "createdAt": 1648450392186,
+ "updatedAt": 1713565132907,
+ "uses": 2,
+ "series": [
+ "liquidations",
+ "5hbxmosl"
+ ],
+ "displayName": "Liquidations",
+ "optionsDefinitions": {},
+ "unsavedChanges": false,
+ "preview": {},
+ "libraryId": "liquidations"
+ },
+ "_7cd5k7cv9ka4qp0s": {
+ "id": "_7cd5k7cv9ka4qp0s",
+ "libraryId": "delta-binance-spot",
+ "name": "BINANCE SPOT USDT",
+ "script": "_vbuy = (BINANCE:btcusdt.vbuy)\n_vsell = (BINANCE:btcusdt.vsell)\n\nvolume = _vbuy+_vsell\na = sma(Math.pow(volume,2),options.length)\nb = Math.pow(sum(volume,options.length),2)/Math.pow(options.length,2)\nstdev = Math.sqrt(a - b)\nbasis = sma(volume, options.length)\ndev = 1 * stdev\ntreshold = basis + dev\n\ndelta = _vbuy - _vsell\n\nplothistogram({ time: time, value: (delta), color: delta > 0 ? ( volume > treshold ? options.upColorHighVol : options.upColorLowVol) : ( volume > treshold ? options.downColorHighVol : options.downColorLowVol)}, title=\"BN.Sâ)",
+ "createdAt": 1649330197719,
+ "updatedAt": 1713583022144,
+ "options": {
+ "priceScaleId": "_7cd5k7cv9ka4qp0s",
+ "upColor": "rgb(100,181,246)",
+ "downColor": "rgb(233,30,99)",
+ "visible": true,
+ "length": 14,
+ "upColorHighVol": "rgb(100,181,246)",
+ "upColorLowVol": "rgb(10,96,162)",
+ "downColorHighVol": "rgb(240,98,146)",
+ "downColorLowVol": "rgb(194,24,91)",
+ "scaleMargins": {
+ "top": 0.14,
+ "bottom": 0.75
+ },
+ "priceFormat": {
+ "type": "volume",
+ "precision": 2,
+ "minMove": 0.01,
+ "auto": true
+ }
+ },
+ "optionsDefinitions": {},
+ "series": [
+ "delta-binance-spot"
+ ],
+ "displayName": "BINANCE SPOT USDT",
+ "unsavedChanges": false
+ },
+ "_kvcozjh87f50e1gs": {
+ "id": "_kvcozjh87f50e1gs",
+ "libraryId": "_7cd5k7cv9ka4qp0s",
+ "name": "BINANCE SPOT FDUSD",
+ "script": "_vbuy = (BINANCE:btcfdusd.vbuy)\n_vsell = (BINANCE:btcfdusd.vsell)\n\nvolume = _vbuy+_vsell\na = sma(Math.pow(volume,2),options.length)\nb = Math.pow(sum(volume,options.length),2)/Math.pow(options.length,2)\nstdev = Math.sqrt(a - b)\nbasis = sma(volume, options.length)\ndev = 1 * stdev\ntreshold = basis + dev\n\ndelta = _vbuy - _vsell\n\nplothistogram({ time: time, value: (delta), color: delta > 0 ? ( volume > treshold ? options.upColorHighVol : options.upColorLowVol) : ( volume > treshold ? options.downColorHighVol : options.downColorLowVol)}, title=\"BN.FDâ)",
+ "createdAt": 1713560579658,
+ "updatedAt": 1713570376824,
+ "options": {
+ "priceScaleId": "_kvcozjh87f50e1gs",
+ "upColor": "rgb(100,181,246)",
+ "downColor": "rgb(233,30,99)",
+ "visible": true,
+ "length": 14,
+ "upColorHighVol": "rgb(100,181,246)",
+ "upColorLowVol": "rgb(10,96,162)",
+ "downColorHighVol": "rgb(240,98,146)",
+ "downColorLowVol": "rgb(194,24,91)",
+ "scaleMargins": {
+ "top": 0.02,
+ "bottom": 0.86
+ },
+ "priceFormat": {
+ "type": "volume",
+ "precision": 2,
+ "minMove": 0.01,
+ "auto": true
+ }
+ },
+ "optionsDefinitions": {},
+ "series": [
+ "_7cd5k7cv9ka4qp0s"
+ ],
+ "displayName": "BINANCE SPOT FDUSD",
+ "unsavedChanges": true
+ },
+ "_mdzsxr1kv4zlvk1x": {
+ "id": "_mdzsxr1kv4zlvk1x",
+ "libraryId": "_kvcozjh87f50e1gs",
+ "name": "CB",
+ "script": "_vbuy = (COINBASE:BTC-USD.vbuy+COINBASE:BTC-USDC.vbuy+COINBASE:BTC-USDT.vbuy)\n_vsell = (COINBASE:BTC-USD.vsell+COINBASE:BTC-USDC.vsell+COINBASE:BTC-USDT.vsell)\n\nvolume = _vbuy+_vsell\na = sma(Math.pow(volume,2),options.length)\nb = Math.pow(sum(volume,options.length),2)/Math.pow(options.length,2)\nstdev = Math.sqrt(a - b)\nbasis = sma(volume, options.length)\ndev = 1 * stdev\ntreshold = basis + dev\n\ndelta = _vbuy - _vsell\n\nplothistogram({ time: time, value: (delta), color: delta > 0 ? ( volume > treshold ? options.upColorHighVol : options.upColorLowVol) : ( volume > treshold ? options.downColorHighVol : options.downColorLowVol)}, title=\"CB\")",
+ "createdAt": 1713560951314,
+ "updatedAt": 1713568342377,
+ "options": {
+ "priceScaleId": "_mdzsxr1kv4zlvk1x",
+ "upColor": "rgb(100,181,246)",
+ "downColor": "rgb(233,30,99)",
+ "visible": true,
+ "length": 14,
+ "upColorHighVol": "rgb(100,181,246)",
+ "upColorLowVol": "rgb(10,96,162)",
+ "downColorHighVol": "rgb(240,98,146)",
+ "downColorLowVol": "rgb(194,24,91)",
+ "scaleMargins": {
+ "top": 0.25,
+ "bottom": 0.63
+ },
+ "priceFormat": {
+ "type": "volume",
+ "precision": 2,
+ "minMove": 0.01,
+ "auto": true
+ }
+ },
+ "optionsDefinitions": {},
+ "displayName": "CB",
+ "series": [
+ "_kvcozjh87f50e1gs"
+ ],
+ "unsavedChanges": true
+ },
+ "_krppkscmyqxrjfyd": {
+ "id": "_krppkscmyqxrjfyd",
+ "libraryId": "_mdzsxr1kv4zlvk1x",
+ "name": "BF",
+ "script": "_vbuy = (BITFINEX:BTCUSD.vbuy+BITFINEX:BTCUST.vbuy)\n_vsell = (BITFINEX:BTCUSD.vsell+BITFINEX:BTCUST.vsell)\n\nvolume = _vbuy+_vsell\na = sma(Math.pow(volume,2),options.length)\nb = Math.pow(sum(volume,options.length),2)/Math.pow(options.length,2)\nstdev = Math.sqrt(a - b)\nbasis = sma(volume, options.length)\ndev = 1 * stdev\ntreshold = basis + dev\n\ndelta = _vbuy - _vsell\n\nplothistogram({ time: time, value: (delta), color: delta > 0 ? ( volume > treshold ? options.upColorHighVol : options.upColorLowVol) : ( volume > treshold ? options.downColorHighVol : options.downColorLowVol)}, title=\"BF\")",
+ "createdAt": 1713561083390,
+ "updatedAt": 1713561205371,
+ "options": {
+ "priceScaleId": "_krppkscmyqxrjfyd",
+ "upColor": "rgb(100,181,246)",
+ "downColor": "rgb(233,30,99)",
+ "visible": true,
+ "length": 14,
+ "upColorHighVol": "rgb(100,181,246)",
+ "upColorLowVol": "rgb(10,96,162)",
+ "downColorHighVol": "rgb(240,98,146)",
+ "downColorLowVol": "rgb(194,24,91)",
+ "scaleMargins": {
+ "top": 0.37,
+ "bottom": 0.51
+ },
+ "priceFormat": {
+ "type": "volume",
+ "precision": 2,
+ "minMove": 0.01,
+ "auto": true
+ }
+ },
+ "optionsDefinitions": {},
+ "series": [
+ "_mdzsxr1kv4zlvk1x"
+ ],
+ "displayName": "BF",
+ "unsavedChanges": true
+ },
+ "_9yf0uhfo6ak1ynmb": {
+ "id": "_9yf0uhfo6ak1ynmb",
+ "libraryId": "bns",
+ "name": "CVD BN.S",
+ "script": "plotline(cum(BINANCE:btcusdt.vbuy - BINANCE:btcusdt.vsell), title=BN.S)",
+ "createdAt": 1713582936533,
+ "updatedAt": 1713583364683,
+ "options": {
+ "priceScaleId": "_9yf0uhfo6ak1ynmb",
+ "scaleMargins": {
+ "top": 0.65,
+ "bottom": 0.14
+ },
+ "color": "rgb(100,181,246)",
+ "priceFormat": {
+ "type": "volume",
+ "precision": 2,
+ "minMove": 0.01,
+ "auto": true
+ },
+ "visible": true
+ },
+ "optionsDefinitions": {},
+ "series": [
+ "bns"
+ ],
+ "displayName": "CVD BN.S",
+ "unsavedChanges": false
+ },
+ "_tvmy75hrwj7387ye": {
+ "id": "_tvmy75hrwj7387ye",
+ "libraryId": "bnfd",
+ "name": "CVD BN.FD",
+ "script": "plotline(cum(BINANCE:btcfdusd.vbuy - BINANCE:btcfdusd.vsell), title=BN.FD)",
+ "createdAt": 1713582982235,
+ "updatedAt": 1713644723785,
+ "options": {
+ "priceScaleId": "_tvmy75hrwj7387ye",
+ "color": "rgb(10,96,162)",
+ "scaleMargins": {
+ "top": 0.65,
+ "bottom": 0.14
+ },
+ "priceFormat": {
+ "type": "volume",
+ "precision": 2,
+ "minMove": 0.01,
+ "auto": true
+ },
+ "visible": true
+ },
+ "optionsDefinitions": {},
+ "series": [
+ "bnfd"
+ ],
+ "displayName": "CVD BN.FD",
+ "unsavedChanges": false
+ },
+ "_shfiapc2wfqzrlq1": {
+ "id": "_shfiapc2wfqzrlq1",
+ "libraryId": "cvd-cb",
+ "name": "CVD CB",
+ "script": "plotline(cum(COINBASE:BTC-USD.vbuy - COINBASE:BTC-USD.vsell), title=CB)",
+ "createdAt": 1713583125412,
+ "updatedAt": 1713583357548,
+ "options": {
+ "priceScaleId": "_shfiapc2wfqzrlq1",
+ "scaleMargins": {
+ "top": 0.65,
+ "bottom": 0.14
+ },
+ "color": "rgb(129,199,132)",
+ "priceFormat": {
+ "type": "volume",
+ "precision": 2,
+ "minMove": 0.01,
+ "auto": true
+ },
+ "visible": true
+ },
+ "optionsDefinitions": {},
+ "series": [
+ "cvd-cb"
+ ],
+ "displayName": "CVD CB",
+ "unsavedChanges": false
+ },
+ "_igcwun84g630m5nk": {
+ "id": "_igcwun84g630m5nk",
+ "libraryId": "cvd-bnp",
+ "name": "CVD BN.P",
+ "script": "plotline(cum(BINANCE_FUTURES:btcusdt.vbuy - BINANCE_FUTURES:btcusdt.vsell), title=BN.P)",
+ "createdAt": 1713583209180,
+ "updatedAt": 1713583450753,
+ "options": {
+ "priceScaleId": "_igcwun84g630m5nk",
+ "scaleMargins": {
+ "top": 0.65,
+ "bottom": 0.14
+ },
+ "color": "rgb(232,219,125)",
+ "priceFormat": {
+ "type": "volume",
+ "precision": 2,
+ "minMove": 0.01,
+ "auto": true
+ },
+ "visible": true
+ },
+ "optionsDefinitions": {},
+ "series": [
+ "cvd-bnp"
+ ],
+ "displayName": "CVD BN.P",
+ "unsavedChanges": false
+ },
+ "_f8y8f2f4126fpzpd": {
+ "id": "_f8y8f2f4126fpzpd",
+ "libraryId": "cvd-bbp",
+ "name": "CVD BB.P",
+ "script": "plotline(cum(BYBIT:BTCUSDT.vbuy - BYBIT:BTCUSDT.vsell), title=BB.P)",
+ "createdAt": 1713583272290,
+ "updatedAt": 1713583383128,
+ "options": {
+ "priceScaleId": "_f8y8f2f4126fpzpd",
+ "scaleMargins": {
+ "top": 0.65,
+ "bottom": 0.14
+ },
+ "color": "rgb(240,98,146)",
+ "priceFormat": {
+ "type": "volume",
+ "precision": 2,
+ "minMove": 0.01,
+ "auto": true
+ },
+ "visible": true
+ },
+ "optionsDefinitions": {},
+ "series": [
+ "cvd-bbp"
+ ],
+ "displayName": "CVD BB.P",
+ "unsavedChanges": false
+ },
+ "_e1esdjt32movky8x": {
+ "id": "_e1esdjt32movky8x",
+ "libraryId": "price",
+ "name": "Price",
+ "script": "plotcandlestick(options.useHeikinAshi ? avg_heikinashi(bar) : options.useGaps ? avg_ohlc_with_gaps(bar) : avg_ohlc(bar))",
+ "createdAt": 1696203148430,
+ "updatedAt": 1713643790838,
+ "options": {
+ "priceScaleId": "_e1esdjt32movky8x",
+ "priceFormat": {
+ "auto": true,
+ "precision": 1,
+ "minMove": 0.1
+ },
+ "priceLineVisible": false,
+ "lastValueVisible": true,
+ "borderVisible": true,
+ "upColor": "rgba(255,255,255,0.5)",
+ "downColor": "rgba(255,255,255,0)",
+ "borderUpColor": "rgba(255,255,255,0.5)",
+ "borderDownColor": "rgba(255,255,255,0.5)",
+ "wickUpColor": "rgba(255,255,255,0.5)",
+ "wickDownColor": "rgba(255,255,255,0.5)",
+ "useGaps": false,
+ "useHeikinAshi": false,
+ "scaleMargins": {
+ "top": 0.49,
+ "bottom": 0.35
+ },
+ "priceLineColor": "rgba(255,255,255,0.5)",
+ "visible": true,
+ "baseLineVisible": false
+ },
+ "optionsDefinitions": {},
+ "series": [
+ "price"
+ ],
+ "displayName": "Price"
+ }
+ },
+ "indicatorOrder": [
+ "_rmg9m2zvgvdyfzgl",
+ "_7cd5k7cv9ka4qp0s",
+ "_kvcozjh87f50e1gs",
+ "_mdzsxr1kv4zlvk1x",
+ "_krppkscmyqxrjfyd",
+ "_9yf0uhfo6ak1ynmb",
+ "_tvmy75hrwj7387ye",
+ "_shfiapc2wfqzrlq1",
+ "_igcwun84g630m5nk",
+ "_f8y8f2f4126fpzpd",
+ "_e1esdjt32movky8x"
+ ],
+ "priceScales": {
+ "right": {
+ "scaleMargins": {
+ "top": 0.49,
+ "bottom": 0.35
+ },
+ "indicators": [
+ "Liquidation Heatmap"
+ ]
+ },
+ "volume_liquidations": {
+ "scaleMargins": {
+ "top": 0.1,
+ "bottom": 0.2
+ },
+ "indicators": [
+ "Liquidations"
+ ]
+ },
+ "volume": {
+ "scaleMargins": {
+ "top": 0.84,
+ "bottom": 0
+ }
+ },
+ "delta-binance-spot": {
+ "scaleMargins": {
+ "top": 0,
+ "bottom": 0.88
+ },
+ "indicators": [
+ "BINANCE SPOT FDUSD"
+ ],
+ "mode": 0
+ },
+ "_7cd5k7cv9ka4qp0s": {
+ "scaleMargins": {
+ "top": 0.14,
+ "bottom": 0.75
+ },
+ "indicators": [
+ "BINANCE SPOT USDT"
+ ],
+ "priceFormat": {
+ "type": "volume",
+ "precision": 2,
+ "minMove": 0.01,
+ "auto": true
+ }
+ },
+ "_kvcozjh87f50e1gs": {
+ "scaleMargins": {
+ "top": 0.02,
+ "bottom": 0.86
+ },
+ "indicators": [
+ "BINANCE SPOT FDUSD"
+ ],
+ "priceFormat": {
+ "type": "volume",
+ "precision": 2,
+ "minMove": 0.01,
+ "auto": true
+ }
+ },
+ "_mdzsxr1kv4zlvk1x": {
+ "scaleMargins": {
+ "top": 0.25,
+ "bottom": 0.63
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+ "mode": 0
+ },
+ "delta-ftx-perps": {
+ "scaleMargins": {
+ "top": 0.88,
+ "bottom": 0.06
+ },
+ "indicators": [
+ "Delta FTX Perps"
+ ],
+ "mode": 0
+ },
+ "delta-bitfinex-spot": {
+ "scaleMargins": {
+ "top": 0.12,
+ "bottom": 0.88
+ },
+ "indicators": [
+ "Delta BitFinex Spot"
+ ],
+ "mode": 0
+ },
+ "delta-bitfinex-perps2": {
+ "scaleMargins": {
+ "top": 0.89,
+ "bottom": 0.05
+ },
+ "indicators": [
+ "Delta BitFinex Perps"
+ ],
+ "mode": 0
+ },
+ "delta-binance-perps": {
+ "scaleMargins": {
+ "top": 0.7,
+ "bottom": 0.17
+ },
+ "indicators": [
+ "Delta Binance Perps"
+ ],
+ "mode": 0
+ },
+ "okx-spot": {
+ "scaleMargins": {
+ "top": 0.09,
+ "bottom": 0.84
+ },
+ "indicators": [
+ "OKX Spot"
+ ]
+ },
+ "price": {
+ "scaleMargins": {
+ "top": 0.11,
+ "bottom": 0.43
+ },
+ "indicators": [
+ "Price",
+ "Price"
+ ],
+ "mode": 0,
+ "priceFormat": {
+ "precision": 1,
+ "minMove": 0.1
+ }
+ },
+ "delta-okx-spot2": {
+ "scaleMargins": {
+ "top": 0.24,
+ "bottom": 0.61
+ },
+ "indicators": [
+ "Delta OKX Spot"
+ ]
+ },
+ "delta-okx-perps2": {
+ "scaleMargins": {
+ "top": 0.83,
+ "bottom": 0.03
+ },
+ "indicators": [
+ "Delta OKX Perps"
+ ]
+ },
+ "cvd-btc-spot": {
+ "scaleMargins": {
+ "top": 0.56,
+ "bottom": 0.36
+ },
+ "indicators": [
+ "CVD ETH Spot"
+ ]
+ },
+ "cvd-btc-perp": {
+ "scaleMargins": {
+ "top": 0.56,
+ "bottom": 0.36
+ },
+ "indicators": [
+ "CVD ETH Perp"
+ ]
+ },
+ "relative-volume copy 1": {
+ "scaleMargins": {
+ "top": 0.55,
+ "bottom": 0.43
+ },
+ "indicators": [
+ "Relative Volume"
+ ]
+ },
+ "single-bar-delta-divergence": {
+ "scaleMargins": {
+ "top": 0,
+ "bottom": 0.06
+ },
+ "indicators": [
+ "Single Bar Delta Divergence"
+ ]
+ },
+ "cvd-eth-perp2": {
+ "scaleMargins": {
+ "top": 0.43,
+ "bottom": 0.49
+ },
+ "indicators": [
+ "CVD ETH Perp"
+ ]
+ },
+ "cvd-eth-spot22": {
+ "scaleMargins": {
+ "top": 0.43,
+ "bottom": 0.49
+ },
+ "indicators": [
+ "CVD ETH Spot"
+ ]
+ },
+ "cvd-okx-s": {
+ "scaleMargins": {
+ "top": 0.1,
+ "bottom": 0.2
+ }
+ },
+ "_lcgvarzto6kaxhsi": {
+ "scaleMargins": {
+ "top": 0.1,
+ "bottom": 0.81
+ },
+ "indicators": [
+ "Delta OKX Spot"
+ ]
+ },
+ "_b4xic3uzz06rtv26": {
+ "scaleMargins": {
+ "top": 0.39,
+ "bottom": 0.52
+ },
+ "indicators": [
+ "Delta ByBit Spot"
+ ],
+ "mode": 0
+ },
+ "_cn9x80j0ttweyqz6": {
+ "scaleMargins": {
+ "top": 0.19,
+ "bottom": 0.72
+ },
+ "indicators": [
+ "Delta KRAKEN Spot"
+ ]
+ },
+ "_zf4aekw47m3y53zx": {
+ "scaleMargins": {
+ "top": 0.27,
+ "bottom": 0.62
+ },
+ "indicators": [
+ "Delta CoinBASE Spot"
+ ]
+ },
+ "_77ryrio4wutjl31c": {
+ "scaleMargins": {
+ "top": 0.4,
+ "bottom": 0.33
+ },
+ "indicators": [
+ "CVD Spot"
+ ]
+ },
+ "_s2ihajoqymk4gckd": {
+ "scaleMargins": {
+ "top": 0.41,
+ "bottom": 0.32
+ },
+ "indicators": [
+ "CVD Perp"
+ ]
+ },
+ "_9zvs80vj4jeitgp5": {
+ "scaleMargins": {
+ "top": 0.78,
+ "bottom": 0.11
+ },
+ "indicators": [
+ "Delta KRAKEN Perp"
+ ]
+ },
+ "_r5cjw16ua6kobv5i": {
+ "scaleMargins": {
+ "top": 0.69,
+ "bottom": 0.25
+ },
+ "indicators": [
+ "Delta Bybit Perp"
+ ]
+ },
+ "_y0s5gdub1mtsl5pv": {
+ "scaleMargins": {
+ "top": 0.58,
+ "bottom": 0.33
+ },
+ "indicators": [
+ "Delta OKX Perp"
+ ]
+ },
+ "_gvqnq2ca8tfq568n": {
+ "scaleMargins": {
+ "top": 0.49,
+ "bottom": 0.43
+ },
+ "indicators": [
+ "Delta Binance Perp"
+ ]
+ },
+ "_6ts9lvh8gqq5lx8q": {
+ "scaleMargins": {
+ "top": 0.24,
+ "bottom": 0.2
+ },
+ "indicators": [
+ "Price ð¶ GIGI"
+ ],
+ "priceFormat": {
+ "precision": 1,
+ "minMove": 0.1
+ }
+ },
+ "cvd-spot": {
+ "scaleMargins": {
+ "top": 0.3,
+ "bottom": 0.45
+ }
+ },
+ "_vcgrf1t9xv14mihs": {
+ "scaleMargins": {
+ "top": 0.89,
+ "bottom": 0.02
+ },
+ "indicators": [
+ "BF2"
+ ],
+ "priceFormat": {
+ "type": "volume",
+ "precision": 2,
+ "minMove": 0.01,
+ "auto": true
+ }
+ }
+ },
+ "layouting": false,
+ "showIndicators": true,
+ "timeframe": "300",
+ "refreshRate": 200,
+ "showAlerts": true,
+ "showAlertsLabel": true,
+ "showLegend": true,
+ "fillGapsWithEmpty": true,
+ "showHorizontalGridlines": false,
+ "horizontalGridlinesColor": "rgba(255,255,255,.1)",
+ "showVerticalGridlines": false,
+ "verticalGridlinesColor": "rgba(255,255,255,.1)",
+ "showWatermark": false,
+ "watermarkColor": "rgba(255,255,255,.1)",
+ "showBorder": true,
+ "borderColor": null,
+ "textColor": null,
+ "showLeftScale": false,
+ "showRightScale": true,
+ "showTimeScale": true,
+ "hiddenMarkets": {},
+ "barSpacing": 32.61306606314917,
+ "_id": "delta",
+ "forceNormalizePrice": false,
+ "navigationState": {
+ "tab": "options",
+ "optionsQuery": "",
+ "fontSizePx": 14
+ }
+ },
+ "chart": {
+ "indicatorsErrors": {},
+ "indicators": {
+ "_pciob9dy9vlp6ksw": {
+ "id": "_pciob9dy9vlp6ksw",
+ "name": "CVD",
+ "options": {
+ "priceScaleId": "cvd",
+ "priceFormat": {
+ "type": "volume"
+ },
+ "color": "#3BCA6D",
+ "scaleMargins": {
+ "top": 0.84,
+ "bottom": 0
+ }
+ },
+ "script": "line(cum(vbuy - vsell))",
+ "createdAt": 1749271162306,
+ "updatedAt": 1749271848162,
+ "description": "Cumulative Volume Delta",
+ "enabled": true,
+ "libraryId": "cvd",
+ "series": [
+ "cvd"
+ ],
+ "optionsDefinitions": {}
+ },
+ "_whcn9xf1lz3xx986": {
+ "enabled": true,
+ "name": "Liquidations",
+ "description": "Liquidations by side",
+ "script": "histogram(lbuy, color=options.upColor)\nhistogram(-lsell, color=options.downColor)",
+ "options": {
+ "priceFormat": {
+ "type": "volume"
+ },
+ "priceScaleId": "volume_liquidations",
+ "upColor": "rgb(255,76,243)",
+ "downColor": "rgb(255,183,77)",
+ "scaleMargins": {
+ "top": 0.75,
+ "bottom": 0.17
+ }
+ },
+ "id": "_whcn9xf1lz3xx986",
+ "createdAt": 1749271162306,
+ "updatedAt": null,
+ "libraryId": "liquidations",
+ "series": [
+ "liquidations",
+ "p3zlvktp"
+ ],
+ "optionsDefinitions": {}
+ },
+ "_d2w8siglj8bhy2p3": {
+ "enabled": true,
+ "name": "Price",
+ "script": "var ohlc = options.useHeikinAshi ? \n avg_heikinashi(bar) : \n options.useGaps ? \n avg_ohlc_with_gaps(bar) : \n avg_ohlc(bar)\n\nplotcandlestick(ohlc)",
+ "options": {
+ "priceScaleId": "right",
+ "priceFormat": {
+ "auto": true,
+ "precision": 1,
+ "minMove": 0.1
+ },
+ "priceLineVisible": true,
+ "lastValueVisible": true,
+ "borderVisible": true,
+ "upColor": "rgb(59,202,109)",
+ "downColor": "rgb(214,40,40)",
+ "borderUpColor": "rgb(59,202,109)",
+ "borderDownColor": "rgb(239,67,82)",
+ "wickUpColor": "rgb(223,211,144)",
+ "wickDownColor": "rgb(239,67,82)",
+ "useGaps": false,
+ "useHeikinAshi": false,
+ "scaleMargins": {
+ "top": 0.04,
+ "bottom": 0.26
+ }
+ },
+ "id": "_d2w8siglj8bhy2p3",
+ "createdAt": 1749271162306,
+ "updatedAt": null,
+ "libraryId": "price",
+ "series": [
+ "price"
+ ],
+ "optionsDefinitions": {},
+ "unsavedChanges": true
+ },
+ "_xmys71x0bt3xmrb1": {
+ "enabled": true,
+ "name": "Volume",
+ "description": "Volume + delta",
+ "script": "if (upColor === 0) {\n if (options.showDelta) {\n upColor = options.upBgColor\n downColor = options.downBgColor\n } else {\n upColor = options.upColor\n downColor = options.downColor\n }\n}\n\nif (options.showDelta) {\n histogram({ time: time, value: Math.abs(vbuy-vsell), color: vbuy - vsell > 0 ? options.upColor : options.downColor})\n}\n\nhistogram({ time: time, value: vbuy + vsell, color: vbuy > vsell ? upColor : downColor })",
+ "options": {
+ "priceFormat": {
+ "type": "volume"
+ },
+ "upColor": "rgb(59,202,109)",
+ "downColor": "rgb(235,30,47)",
+ "priceScaleId": "volume",
+ "scaleMargins": {
+ "top": 0.84,
+ "bottom": 0
+ },
+ "showDelta": true,
+ "upBgColor": "rgba(59,202,109,0.5)",
+ "downBgColor": "rgba(235,30,47,0.5)"
+ },
+ "id": "_xmys71x0bt3xmrb1",
+ "createdAt": 1749271162306,
+ "updatedAt": null,
+ "libraryId": "volume",
+ "series": [
+ "volume",
+ "x4q2m953"
+ ],
+ "optionsDefinitions": {}
+ }
+ },
+ "indicatorOrder": [
+ "_pciob9dy9vlp6ksw",
+ "_whcn9xf1lz3xx986",
+ "_d2w8siglj8bhy2p3",
+ "_xmys71x0bt3xmrb1"
+ ],
+ "priceScales": {
+ "right": {
+ "scaleMargins": {
+ "top": 0.04,
+ "bottom": 0.26
+ },
+ "priceFormat": {
+ "precision": 1,
+ "minMove": 0.1
+ }
+ },
+ "cvd": {
+ "scaleMargins": {
+ "top": 0.84,
+ "bottom": 0
+ }
+ },
+ "volume_liquidations": {
+ "scaleMargins": {
+ "top": 0.75,
+ "bottom": 0.17
+ }
+ },
+ "volume": {
+ "scaleMargins": {
+ "top": 0.84,
+ "bottom": 0
+ }
+ }
+ },
+ "layouting": false,
+ "showIndicators": true,
+ "timeframe": 5,
+ "refreshRate": 1000,
+ "showAlerts": true,
+ "showAlertsLabel": true,
+ "showLegend": true,
+ "fillGapsWithEmpty": false,
+ "showHorizontalGridlines": false,
+ "horizontalGridlinesColor": "rgba(255,255,255,.1)",
+ "showVerticalGridlines": false,
+ "verticalGridlinesColor": "rgba(255,255,255,.1)",
+ "showWatermark": true,
+ "watermarkColor": "rgba(255,255,255,.033)",
+ "showBorder": true,
+ "borderColor": null,
+ "textColor": null,
+ "showLeftScale": false,
+ "showRightScale": true,
+ "showTimeScale": true,
+ "hiddenMarkets": {},
+ "barSpacing": 4.6914145078209195,
+ "_id": "chart",
+ "navigationState": {
+ "tab": "script",
+ "optionsQuery": "",
+ "fontSizePx": 14
+ },
+ "forceNormalizePrice": false
+ }
+ }
+}
\ No newline at end of file
diff --git a/python/valuecell/agents/common/trading/data/interfaces.py b/python/valuecell/agents/common/trading/data/interfaces.py
index cbad57a47..c6710b850 100644
--- a/python/valuecell/agents/common/trading/data/interfaces.py
+++ b/python/valuecell/agents/common/trading/data/interfaces.py
@@ -3,7 +3,11 @@
from abc import ABC, abstractmethod
from typing import List
-from valuecell.agents.common.trading.models import Candle, MarketSnapShotType
+from valuecell.agents.common.trading.models import (
+ Candle,
+ DataSourceImage,
+ MarketSnapShotType,
+)
# Contracts for market data sources (module-local abstract interfaces).
# These are plain ABCs (not Pydantic models) so implementations can be
@@ -42,3 +46,18 @@ async def get_market_snapshot(self, symbols: List[str]) -> MarketSnapShotType:
"""
raise NotImplementedError
+
+
+class BaseScreenshotDataSource(ABC):
+ """
+ Abstract base class for screenshot data sources.
+ """
+
+ @abstractmethod
+ async def capture(self, *args, **kwargs) -> List[DataSourceImage]:
+ """
+ Captures a screenshot and returns a list of `DataSourceImage` instances describing the
+ captured bytes and/or saved file path. Implementations should standardize
+ on `DataSourceImage.content` (bytes) and/or `DataSourceImage.filepath`.
+ """
+ raise NotImplementedError
diff --git a/python/valuecell/agents/common/trading/data/screenshot.py b/python/valuecell/agents/common/trading/data/screenshot.py
new file mode 100644
index 000000000..24922e14a
--- /dev/null
+++ b/python/valuecell/agents/common/trading/data/screenshot.py
@@ -0,0 +1,287 @@
+import asyncio
+import os
+from datetime import datetime
+from typing import List, Optional
+
+import aiofiles
+from loguru import logger
+from playwright.async_api import Browser, Page, Playwright, async_playwright
+
+from valuecell.agents.common.trading.models import DataSourceImage
+from valuecell.utils.path import get_screenshot_path
+
+from ..models import InstrumentRef
+from ..utils import normalize_symbol_tradingview
+from .interfaces import BaseScreenshotDataSource
+from .utils import generate_tradingview_html
+
+
+class PlaywrightScreenshotDataSource(BaseScreenshotDataSource):
+ """
+ Concrete implementation using Playwright.
+ Implements Async Context Manager protocol for automatic setup and teardown.
+ """
+
+ def __init__(
+ self,
+ target_url: Optional[str] = None,
+ page_content: Optional[str] = None,
+ instrument: Optional[InstrumentRef] = None,
+ ):
+ """
+ Initializes configuration.
+ """
+ self.target_url = target_url
+ self.page_content = page_content
+ self.instrument = instrument
+
+ self.playwright: Optional[Playwright] = None
+ self.browser: Optional[Browser] = None
+ self.page: Optional[Page] = None
+
+ async def __aenter__(self):
+ """
+ Magic method for 'async with'.
+ Starts the browser, navigates to the URL, and performs the setup automation.
+ """
+ # Delegate to explicit open() so callers can avoid repeated __aenter__ overhead
+ return await self.open()
+
+ async def __aexit__(self, exc_type, exc_val, exc_tb):
+ """
+ Magic method for 'async with'.
+ Handles cleanup of browser resources.
+ """
+ if exc_type:
+ logger.error(f"Exiting session due to exception: {exc_val}")
+
+ await self.close()
+
+ async def _cleanup(self):
+ """
+ Internal helper to close browser resources.
+ """
+ if self.browser:
+ await self.browser.close()
+ if self.playwright:
+ await self.playwright.stop()
+
+ async def _setup(self):
+ if self.target_url:
+ logger.info(f"Navigating to {self.target_url}")
+ await self.page.goto(self.target_url)
+ return
+
+ if self.page_content:
+ logger.info("Setting page content from provided HTML.")
+ await self.page.set_content(self.page_content)
+ return
+
+ async def open(self):
+ """Explicit initialization to support one-time setup.
+
+ Returns:
+ self: the initialized data source (same as __aenter__ would).
+ """
+ try:
+ logger.info("Initializing Playwright session...")
+ self.playwright = await async_playwright().start()
+ self.browser = await self.playwright.chromium.launch(headless=True)
+
+ context = await self.browser.new_context(
+ viewport={"width": 1600, "height": 900}
+ )
+ self.page = await context.new_page()
+
+ await self._setup()
+
+ return self
+
+ except Exception as e:
+ logger.error(f"Error during initialization: {e}")
+ # Ensure cleanup happens if initialization fails mid-way
+ await self._cleanup()
+ raise e
+
+ async def close(self):
+ """Explicit cleanup to support one-time teardown.
+
+ Calls internal cleanup helpers and logs session close.
+ """
+ await self._cleanup()
+ logger.info("Session closed.")
+
+ async def capture(self, *args, **kwargs) -> List[DataSourceImage]:
+ """
+ Captures the current state of the page.
+ """
+ if not self.page:
+ logger.error("Page is not initialized. Cannot capture screenshot.")
+ return []
+
+ try:
+ timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+ logger.info(f"Capturing screenshot at {timestamp}...")
+
+ # Capture screenshot bytes
+ screenshot_bytes = await self.page.screenshot(full_page=True)
+
+ # Persist bytes to screenshots directory asynchronously using aiofiles
+ fmt = "png"
+ full_path = os.path.join(
+ get_screenshot_path(), f"screenshot_{timestamp}.{fmt}"
+ )
+
+ async with aiofiles.open(full_path, "wb") as fh:
+ await fh.write(screenshot_bytes)
+
+ # Build DataSourceImage with both bytes and file path
+ ds_image = DataSourceImage(
+ url=None,
+ filepath=full_path,
+ content=screenshot_bytes,
+ instrument=self.instrument,
+ )
+
+ logger.info(f"Screenshot captured and saved to {full_path}")
+ return [ds_image]
+
+ except Exception as e:
+ logger.error(f"Failed to capture screenshot: {e}")
+ return []
+
+
+class AggrScreenshotDataSource(PlaywrightScreenshotDataSource):
+ """
+ Composite data source that aggregates multiple screenshot data sources.
+ """
+
+ def __init__(
+ self,
+ target_url: str,
+ file_path: str,
+ instrument: Optional[InstrumentRef] = None,
+ ):
+ super().__init__(target_url, instrument=instrument)
+ self.file_path = file_path
+
+ # Ensure dummy file exists if not present
+ if not os.path.exists(self.file_path):
+ logger.warning(
+ f"File {self.file_path} not found. Creating empty JSON file."
+ )
+ with open(self.file_path, "w") as f:
+ f.write("{}")
+
+ async def _setup(self):
+ logger.info(f"Navigating to {self.target_url}")
+ await self.page.goto(self.target_url)
+
+ logger.info("Waiting for core UI elements...")
+ # Wait for the green menu button to ensure page load
+ menu_btn = self.page.locator("#menu .menu__button")
+ await menu_btn.wait_for(state="visible", timeout=60000)
+
+ logger.info("Page loaded. Executing setup sequence.")
+
+ # 1. Click Menu
+ await menu_btn.click()
+
+ # 2. Click Settings
+ await self.page.get_by_text("Settings", exact=True).click()
+
+ # 3. Click New
+ await self.page.locator("button").filter(has_text="New").click()
+
+ # 4. Handle File Upload
+ logger.info("Uploading file...")
+ async with self.page.expect_file_chooser() as fc_info:
+ await self.page.get_by_text("Upload template file").click()
+
+ file_chooser = await fc_info.value
+ await file_chooser.set_files(self.file_path)
+
+ # Wait slightly for UI render
+ await asyncio.sleep(1)
+
+ # 5. Click IMPORT
+ logger.info("Confirming import...")
+ import_btn = self.page.locator("button").filter(has_text="IMPORT")
+ await import_btn.wait_for(state="visible")
+ await import_btn.click()
+
+ logger.info("Import successful. Waiting for modal to close...")
+ await asyncio.sleep(3)
+
+
+class TradingViewScreenshotDataSource(PlaywrightScreenshotDataSource):
+ """Screenshot data source that renders a TradingView widget for a given symbol
+
+ It generates an HTML page containing the TradingView advanced-chart widget,
+ waits for the iframe and a short stabilization period, then captures only
+ the widget container element to produce a focused screenshot.
+ """
+
+ def __init__(
+ self,
+ symbol: str,
+ instrument: Optional[InstrumentRef] = None,
+ ) -> None:
+ symbol = normalize_symbol_tradingview(symbol)
+ page_content = generate_tradingview_html(symbol)
+ super().__init__(
+ target_url=None, page_content=page_content, instrument=instrument
+ )
+
+ self.symbol = symbol
+
+ async def _setup(self):
+ # Build full HTML wrapper and set as page content
+ await self.page.set_content(self.page_content)
+ logger.info("Loading TradingView widget for %s", self.symbol)
+
+ # Wait for the iframe to attach
+ await self.page.wait_for_selector("iframe", timeout=15000)
+
+ # Wait for visual stability (widgets often animate/fetch data)
+ await self.page.wait_for_timeout(3000)
+
+ # Ensure the container is visible
+ widget_locator = self.page.locator(".tradingview-widget-container")
+ await widget_locator.wait_for(state="visible", timeout=10000)
+
+ async def capture(self, *args, **kwargs) -> List[DataSourceImage]:
+ if not self.page:
+ logger.error("Page is not initialized. Cannot capture screenshot.")
+ return []
+
+ try:
+ timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+ logger.info(
+ f"Capturing TradingView widget screenshot for {self.symbol} at {timestamp}..."
+ )
+
+ widget_locator = self.page.locator(".tradingview-widget-container")
+ # Capture only the widget element to reduce noise
+ png_bytes = await widget_locator.screenshot()
+
+ fmt = "png"
+ full_path = os.path.join(
+ get_screenshot_path(), f"tradingview_{self.symbol}_{timestamp}.{fmt}"
+ )
+ async with aiofiles.open(full_path, "wb") as fh:
+ await fh.write(png_bytes)
+
+ ds_image = DataSourceImage(
+ url=None,
+ filepath=full_path,
+ content=png_bytes,
+ instrument=self.instrument,
+ )
+
+ logger.info(f"TradingView screenshot saved to {full_path}")
+ return [ds_image]
+
+ except Exception as e:
+ logger.error(f"Failed to capture TradingView screenshot: {e}")
+ return []
diff --git a/python/valuecell/agents/common/trading/data/utils.py b/python/valuecell/agents/common/trading/data/utils.py
new file mode 100644
index 000000000..f581f6166
--- /dev/null
+++ b/python/valuecell/agents/common/trading/data/utils.py
@@ -0,0 +1,45 @@
+from pydantic import BaseModel, Field
+
+
+class TradingViewConfig(BaseModel):
+ symbol: str
+ allow_symbol_change: bool = False
+ calendar: bool = False
+ details: bool = False
+ hide_side_toolbar: bool = True
+ hide_top_toolbar: bool = True
+ hide_legend: bool = False
+ hide_volume: bool = False
+ hotlist: bool = False
+ interval: str = "D"
+ locale: str = "en"
+ save_image: bool = False
+ style: str = "1"
+ theme: str = "light"
+ timezone: str = "Etc/UTC"
+ backgroundColor: str = "#ffffff"
+ gridColor: str = "rgba(46, 46, 46, 0.06)"
+ watchlist: list[str] = Field(default_factory=list)
+ withdateranges: bool = False
+ compareSymbols: list[str] = Field(default_factory=list)
+ studies: list[str] = Field(default_factory=list)
+ autosize: bool = True
+
+
+def generate_tradingview_html(symbol: str) -> str:
+ widget_config = TradingViewConfig(symbol=symbol)
+ config_json = widget_config.model_dump_json(indent=2)
+ html_content = f"""
+
+
+
+
+
+"""
+ return html_content
diff --git a/python/valuecell/agents/common/trading/features/image.py b/python/valuecell/agents/common/trading/features/image.py
new file mode 100644
index 000000000..ab2aa8656
--- /dev/null
+++ b/python/valuecell/agents/common/trading/features/image.py
@@ -0,0 +1,126 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+from agno.agent import Agent as AgnoAgent
+from agno.media import Image as AgnoImage
+from agno.models.base import Model as AgnoModel
+from loguru import logger
+
+from valuecell.utils import model as model_utils
+from valuecell.utils.ts import get_current_timestamp_ms
+
+from ..constants import (
+ FEATURE_GROUP_BY_IMAGE_ANALYSIS,
+ FEATURE_GROUP_BY_KEY,
+)
+from ..models import (
+ FeatureVector,
+ UserRequest,
+)
+from .interfaces import (
+ ImageBasedFeatureComputer,
+)
+
+if TYPE_CHECKING:
+ from valuecell.agents.common.trading.models import DataSourceImage
+
+
+class MLLMImageFeatureComputer(ImageBasedFeatureComputer):
+ """Image feature computer using an MLLM (Gemini via agno Agent).
+
+ Consumes dashboard screenshots and extracts structured trading insights,
+ returning a single `FeatureVector` with textual features. Instrument is
+ left unset (market-wide analysis).
+ """
+
+ def __init__(self, model: AgnoModel, prompt: str) -> None:
+ """Initialize with a pre-built `Agent` instance.
+
+ The agent's `.model` and `.model.id` are inspected for provider/model
+ metadata.
+ """
+ self._model = model
+ self._prompt = prompt
+ self._agent = AgnoAgent(
+ model=model,
+ instructions=[self._prompt],
+ markdown=True,
+ )
+
+ @classmethod
+ def from_request(
+ cls, request: UserRequest, prompt: str
+ ) -> "MLLMImageFeatureComputer":
+ """Create an instance from an `LLMModelConfig`.
+
+ Builds a model via `model_utils.create_model_with_provider` and
+ constructs an `Agent` that will be reused for image analysis.
+ """
+ llm_cfg = request.llm_model_config
+ created_model = model_utils.create_model_with_provider(
+ provider=llm_cfg.provider,
+ model_id=llm_cfg.model_id,
+ api_key=llm_cfg.api_key,
+ )
+
+ # Validate that the model declares image support in config
+ if not model_utils.supports_model_images(llm_cfg.provider, llm_cfg.model_id):
+ raise RuntimeError(
+ f"Model {llm_cfg.model_id} from provider {llm_cfg.provider} does not declare support for images"
+ )
+
+ return cls(created_model, prompt)
+
+ async def compute_features(
+ self,
+ images: Optional[List["DataSourceImage"]] = None,
+ meta: Optional[Dict[str, Any]] = None,
+ ) -> List[FeatureVector]:
+ if not images:
+ logger.warning("No images provided for image feature computation")
+ return []
+
+ logger.info("Running MLLM analysis on provided images: {}", images)
+ # Convert DataSourceImage -> agno.media.Image for the agent
+ agno_images: List[AgnoImage] = []
+ for ds in images:
+ try:
+ if content := ds.content:
+ agno_images.append(AgnoImage(content=content))
+ continue
+
+ if filepath := ds.filepath:
+ agno_images.append(AgnoImage(filepath=filepath))
+ continue
+
+ if url := ds.url:
+ agno_images.append(AgnoImage(url=url))
+
+ except Exception as e:
+ logger.warning("Failed to convert DataSourceImage to agno.Image: {}", e)
+
+ resp = await self._agent.arun(
+ "analyze the trading dashboard configuration in the provided image and generate a brief report.",
+ images=agno_images,
+ )
+
+ content: str = getattr(resp, "content", "") or ""
+ logger.info("MLLM analysis complete: {}", content)
+
+ # Store only the raw markdown report as requested.
+ values: Dict[str, Any] = {"report_markdown": content}
+ meta = meta or {}
+ # TODO: include image metadata such as filepath, url
+ fv_meta = {
+ FEATURE_GROUP_BY_KEY: FEATURE_GROUP_BY_IMAGE_ANALYSIS,
+ **meta,
+ }
+ instrument = images[0].instrument
+ fv = FeatureVector(
+ ts=get_current_timestamp_ms(),
+ instrument=instrument,
+ values=values,
+ meta=fv_meta,
+ )
+ return [fv]
diff --git a/python/valuecell/agents/common/trading/features/interfaces.py b/python/valuecell/agents/common/trading/features/interfaces.py
index 20569fc5b..e8d08ba34 100644
--- a/python/valuecell/agents/common/trading/features/interfaces.py
+++ b/python/valuecell/agents/common/trading/features/interfaces.py
@@ -1,7 +1,7 @@
from __future__ import annotations
from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
from valuecell.agents.common.trading.models import (
Candle,
@@ -9,6 +9,10 @@
FeatureVector,
)
+if TYPE_CHECKING:
+ # Only for type hints to avoid hard dependency at runtime
+ from valuecell.agents.common.trading.models import DataSourceImage
+
# Contracts for feature computation (module-local abstract interfaces).
# Plain ABCs (not Pydantic) to keep implementations lightweight.
@@ -39,6 +43,31 @@ def compute_features(
raise NotImplementedError
+class ImageBasedFeatureComputer(ABC):
+ """Abstract base for image-based feature computers.
+
+ Implementations consume one or more images (screenshots, dashboard panes)
+ and return domain FeatureVector objects. The concrete implementations may
+ call external vision/LLM services.
+ """
+
+ @abstractmethod
+ async def compute_features(
+ self,
+ images: Optional[List["DataSourceImage"]] = None,
+ meta: Optional[Dict[str, Any]] = None,
+ ) -> List[FeatureVector]:
+ """Build feature vectors from the provided images.
+
+ Args:
+ images: list of `DataSourceImage` objects produced by data sources.
+ meta: optional metadata such as instrument or timestamps.
+ Returns:
+ A list of `FeatureVector` items.
+ """
+ raise NotImplementedError
+
+
class BaseFeaturesPipeline(ABC):
"""Abstract pipeline that produces feature vectors (including market features)."""
@@ -51,3 +80,22 @@ async def build(self) -> FeaturesPipelineResult:
into this call.
"""
raise NotImplementedError
+
+ @abstractmethod
+ async def open(self) -> None:
+ """Optional one-time initialization for long-lived resources.
+
+ Implementations may open network/browser sessions or warm caches here.
+ This method will be called by the runtime when available; callers may
+ ignore if not needed.
+ """
+ pass
+
+ @abstractmethod
+ async def close(self) -> None:
+ """Optional cleanup for resources allocated in `open()`.
+
+ Called by the runtime during shutdown to release resources (e.g., close
+ browser, stop background tasks). Implementations should make this idempotent.
+ """
+ pass
diff --git a/python/valuecell/agents/common/trading/features/pipeline.py b/python/valuecell/agents/common/trading/features/pipeline.py
index 547fd6ebd..1dadd5f7f 100644
--- a/python/valuecell/agents/common/trading/features/pipeline.py
+++ b/python/valuecell/agents/common/trading/features/pipeline.py
@@ -9,7 +9,7 @@
from __future__ import annotations
import asyncio
-import itertools
+from pathlib import Path
from typing import List, Optional
from loguru import logger
@@ -23,12 +23,16 @@
from ..data.interfaces import BaseMarketDataSource
from ..data.market import SimpleMarketDataSource
+from ..data.screenshot import AggrScreenshotDataSource, PlaywrightScreenshotDataSource
+from ..models import InstrumentRef
from .candle import SimpleCandleFeatureComputer
+from .image import MLLMImageFeatureComputer
from .interfaces import (
BaseFeaturesPipeline,
CandleBasedFeatureComputer,
)
from .market_snapshot import MarketSnapshotFeatureComputer
+from .prompts import AGGR_PROMPT
class DefaultFeaturesPipeline(BaseFeaturesPipeline):
@@ -42,18 +46,26 @@ def __init__(
candle_feature_computer: CandleBasedFeatureComputer,
market_snapshot_computer: MarketSnapshotFeatureComputer,
candle_configurations: Optional[List[CandleConfig]] = None,
+ screenshot_data_source: Optional[PlaywrightScreenshotDataSource] = None,
+ image_feature_computer: Optional[MLLMImageFeatureComputer] = None,
) -> None:
self._request = request
self._market_data_source = market_data_source
self._candle_feature_computer = candle_feature_computer
self._symbols = list(dict.fromkeys(request.trading_config.symbols))
self._market_snapshot_computer = market_snapshot_computer
- self._candle_configurations = candle_configurations
+ self._screenshot_data_source = screenshot_data_source
+ self._image_feature_computer = image_feature_computer
self._candle_configurations = candle_configurations or [
- CandleConfig(interval="1s", lookback=60 * 3),
CandleConfig(interval="1m", lookback=60 * 4),
]
+ async def open(self) -> None:
+ """Open any long-lived resources needed by the pipeline."""
+
+ if self._screenshot_data_source is not None:
+ await self._screenshot_data_source.open()
+
async def build(self) -> FeaturesPipelineResult:
"""
Fetch candles and market snapshot, compute feature vectors concurrently,
@@ -80,27 +92,66 @@ async def _fetch_market_features() -> List[FeatureVector]:
logger.info(
f"Starting concurrent data fetching for {len(self._candle_configurations)} candle sets and markets snapshot..."
)
- tasks = [
- _fetch_candles(config.interval, config.lookback)
- for config in self._candle_configurations
- ]
- tasks.append(_fetch_market_features())
- # results = [ [candle_features_1], [candle_features_2], ..., [market_features] ]
- results = await asyncio.gather(*tasks)
+ # Create named tasks so we don't depend on result ordering.
+ tasks_map: dict[str, asyncio.Task] = {}
+
+ for idx, config in enumerate(self._candle_configurations):
+ name = f"candles:{config.interval}:{idx}"
+ coro = _fetch_candles(config.interval, config.lookback)
+ tasks_map[name] = asyncio.create_task(coro)
+
+ # market snapshot task
+ tasks_map["market"] = asyncio.create_task(_fetch_market_features())
+
+ # Optionally fetch and compute image-based features if providers are set
+ if (
+ self._screenshot_data_source is not None
+ and self._image_feature_computer is not None
+ ):
+
+ async def _fetch_image_features() -> List[FeatureVector]:
+ try:
+ images = await self._screenshot_data_source.capture()
+ return await self._image_feature_computer.compute_features(
+ images=images
+ )
+ except Exception as e:
+ logger.error(f"Failed to capture screenshot: {e}")
+ return []
+
+ tasks_map["image"] = asyncio.create_task(_fetch_image_features())
+
+ # Await all tasks and then collect results by name
+ await asyncio.gather(*tasks_map.values())
logger.info("Concurrent data fetching complete.")
- market_features: List[FeatureVector] = results.pop()
+ results_map: dict[str, List[FeatureVector]] = {
+ name: task.result() for name, task in tasks_map.items()
+ }
- # Flatten the list of lists of candle features
- candle_features: List[FeatureVector] = list(
- itertools.chain.from_iterable(results)
- )
+ # Flatten candle features from all candle tasks
+ candle_features: List[FeatureVector] = []
+ for name, feats in results_map.items():
+ if name.startswith("candles:"):
+ candle_features.extend(feats)
+
+ # Append market features if available
+ market_features: List[FeatureVector] = results_map.get("market", [])
+
+ # Append image-derived features if available
+ image_features: List[FeatureVector] = results_map.get("image", [])
candle_features.extend(market_features)
+ candle_features.extend(image_features)
return FeaturesPipelineResult(features=candle_features)
+ async def close(self) -> None:
+ """Close any long-lived resources created by the pipeline."""
+ if self._screenshot_data_source is not None:
+ await self._screenshot_data_source.close()
+
@classmethod
def from_request(cls, request: UserRequest) -> DefaultFeaturesPipeline:
"""Factory creating the default pipeline from a user request."""
@@ -109,9 +160,35 @@ def from_request(cls, request: UserRequest) -> DefaultFeaturesPipeline:
)
candle_feature_computer = SimpleCandleFeatureComputer()
market_snapshot_computer = MarketSnapshotFeatureComputer()
+
+ try:
+ image_feature_computer = MLLMImageFeatureComputer.from_request(
+ request, prompt=AGGR_PROMPT
+ )
+ charts_json = (
+ Path(__file__).parent.parent
+ / "data"
+ / "configs"
+ / "aggr"
+ / "charts.json"
+ )
+ screenshot_data_source = AggrScreenshotDataSource(
+ target_url="https://aggr.trade",
+ file_path=str(charts_json),
+ instrument=InstrumentRef(symbol="BTCUSD"),
+ )
+ except Exception as e:
+ logger.warning(
+ f"Image feature computer could not be initialized: {e}. Proceeding without image features."
+ )
+ image_feature_computer = None
+ screenshot_data_source = None
+
return cls(
request=request,
market_data_source=market_data_source,
candle_feature_computer=candle_feature_computer,
market_snapshot_computer=market_snapshot_computer,
+ image_feature_computer=image_feature_computer,
+ screenshot_data_source=screenshot_data_source,
)
diff --git a/python/valuecell/agents/common/trading/features/prompts.py b/python/valuecell/agents/common/trading/features/prompts.py
new file mode 100644
index 000000000..8ae3ad233
--- /dev/null
+++ b/python/valuecell/agents/common/trading/features/prompts.py
@@ -0,0 +1,75 @@
+AGGR_PROMPT: str = """
+# Role
+You are an expert High-Frequency Trader (HFT) and Order Flow Analyst specializing in crypto market microstructure. You are analyzing a dashboard from Aggr.trade.
+
+# Visual Context
+The image displays three vertical panes:
+1. **Top (Price & Global CVD):** 5s candles, Aggregate Volume, Liquidations (bright bars), and Global CVD line.
+2. **Middle (Delta Grid):** Net Delta per exchange/pair (5m timeframe). Key: Spot (S) vs. Perps (P).
+3. **Bottom (Exchange CVDs):** Cumulative Volume Delta lines for individual exchanges (15m timeframe).
+ * *Legend Assumption:* Cyan/Blue = Coinbase (Spot); Yellow/Red = Binance (Spot/Perps).
+
+# Analysis Objectives
+Please analyze the order flow dynamics and provide a scalping strategy based on the following:
+
+1. **Spot vs. Perp Dynamics:**
+ * Is the price action driven by Spot demand (e.g., Coinbase buying) or Perp speculation?
+ * Identify any **"Spot Premium"** or **"Perp Discount"** behavior.
+
+2. **Absorption & Divergences (CRITICAL):**
+ * Look for **"Passive Absorption"**: Are we seeing aggressive selling (Red Delta/CVD) resulting in stable or rising prices?
+ * Look for **"CVD Divergences"**: Is Price making Higher Highs while Global/Binance CVD makes Lower Highs?
+
+3. **Exchange Specific Flows:**
+ * Compare **Coinbase Spot (Smart Money)** vs. **Binance Perps (Retail/Speculative)**. Are they correlated or fighting each other?
+
+# Output Format
+Provide a concise professional report:
+* **Market State:** (e.g., Spot-Led Grind, Short Squeeze, Liquidation Cascade)
+* **Key Observation:** (One sentence on the most critical anomaly, e.g., "Coinbase bidding while Binance dumps.")
+* **Trade Setup:**
+ * **Bias:** [LONG / SHORT / NEUTRAL]
+ * **Entry Trigger:** (e.g., "Enter on retest of VWAP with absorption.")
+ * **Invalidation:** (Where does the thesis fail?)
+"""
+
+TRADINGVIEW_WIDGET_PROMPT: str = """
+# Role
+You are an expert Technical Analyst and Swing Trader specializing in Price Action and Market Structure. You are analyzing a chart screenshot from a TradingView Widget.
+
+# Visual Context
+The image displays a standard candlestick chart (likely Crypto or Stock asset).
+1. **Price Action:** Candlesticks showing Open, High, Low, Close data.
+2. **Volume:** Vertical bars at the bottom representing trading activity.
+3. **Timeframe:** Identify the timeframe from the top left (e.g., 1D, 4H, 15m).
+4. **Asset:** Identify the ticker symbol (e.g., DOGE/USDT, BTC/USD).
+
+# Analysis Objectives
+Please analyze the chart structure and provide a trading plan based on the following pillars:
+
+1. **Market Structure & Trend Identification:**
+ * Determine the **Current Trend**: Is the market in an Uptrend (Higher Highs/Higher Lows), Downtrend (Lower Highs/Lower Lows), or Consolidation (Ranging)?
+ * Identify the **Market Phase**: Accumulation, Markup, Distribution, or Decline?
+
+2. **Key Levels (Support & Resistance):**
+ * Identify major **Horizontal Levels**: Where has price historically bounced or rejected?
+ * Identify **Supply/Demand Zones**: Look for areas of explosive moves that price is revisiting.
+ * *Optional:* Note any visible trendlines or chart patterns (e.g., Head & Shoulders, Flags, Wedges).
+
+3. **Volume & Candlestick Analysis (VSA):**
+ * **Volume Anomalies:** Is there high volume at lows (Stopping Volume) or high volume at highs (Selling Pressure)?
+ * **Candlestick Signals:** Identify specific patterns on the most recent candles (e.g., Pin Bar, Engulfing, Doji) that suggest reversal or continuation.
+ * **Momentum:** Are the candles getting larger (increasing momentum) or smaller (loss of momentum)?
+
+# Output Format
+Provide a concise, actionable trading report:
+
+* **Market Context:** (e.g., "DOGE is consolidating at daily support after a 30% correction.")
+* **Technical Signal:** (One sentence on the most important technical factor, e.g., "Bullish Engulfing candle formed on high volume at the 0.14 support level.")
+* **Trade Setup:**
+ * **Bias:** [BULLISH / BEARISH / NEUTRAL]
+ * **Key Level:** (The price level that matters most right now).
+ * **Action Plan:** (e.g., "Wait for a daily close above 0.16 to confirm reversal," or "Short on rejection of 0.18.")
+ * **Invalidation:** (A price level that proves this thesis wrong, e.g., "Close below 0.13".)
+
+"""
diff --git a/python/valuecell/agents/common/trading/models.py b/python/valuecell/agents/common/trading/models.py
index 983123674..bfc7b4ef4 100644
--- a/python/valuecell/agents/common/trading/models.py
+++ b/python/valuecell/agents/common/trading/models.py
@@ -1,5 +1,6 @@
from dataclasses import dataclass
from enum import Enum
+from pathlib import Path
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field, field_validator, model_validator
@@ -384,7 +385,7 @@ class FeatureVector(BaseModel):
...,
description="Feature vector timestamp in ms",
)
- instrument: InstrumentRef
+ instrument: Optional[InstrumentRef]
values: Dict[CommonKeyType, CommonValueType | List[CommonValueType]] = Field(
default_factory=dict, description="Feature name to numeric value"
)
@@ -947,3 +948,15 @@ class DecisionCycleResult:
history_records: List[HistoryRecord]
digest: TradeDigest
portfolio_view: PortfolioView
+
+
+@dataclass
+class DataSourceImage:
+ url: Optional[str] = None # Remote location
+ filepath: Optional[Path | str] = None # Local file path
+ content: Optional[bytes] = None # Raw image bytes (standardized to bytes)
+
+ instrument: Optional[InstrumentRef] = None # Associated instrument, if any
+
+ def __repr__(self):
+ return f"DataSourceImage(url={self.url}, filepath={self.filepath}, content={'' if self.content else None}, instrument={self.instrument})"
diff --git a/python/valuecell/agents/common/trading/utils.py b/python/valuecell/agents/common/trading/utils.py
index 6742cead2..f45c74d89 100644
--- a/python/valuecell/agents/common/trading/utils.py
+++ b/python/valuecell/agents/common/trading/utils.py
@@ -182,6 +182,40 @@ def normalize_symbol(symbol: str) -> str:
return base_symbol
+def normalize_symbol_tradingview(symbol: str) -> str:
+ """Normalize symbol format for TradingView widgets.
+
+ Rules implemented:
+ - If the input contains a colon ("A/B:B" style), take the portion
+ to the left of the first colon, remove separators (`-` and `/`),
+ convert to upper-case and append the suffix `.P`.
+ Example: `BTC-USDT:USDT` or `BTC/USDT:PERP` -> `BTCUSDT.P`.
+ - Otherwise remove separators (`-` and `/`) and convert to upper-case.
+ Examples: `BTC-USD` -> `BTCUSD`, `ETH/USDT` -> `ETHUSDT`.
+
+ Args:
+ symbol: Input symbol string (e.g., 'BTC-USD', 'BTC/USDT', 'BTC-USDT:USDT', 'BTC/USDT:PERP').
+
+ Returns:
+ Normalized TradingView symbol string in upper-case. The `.P` suffix
+ is used for inputs that include a colon to indicate the right-hand
+ side (perpetual/venue) was present and was stripped from the output.
+ """
+ if not symbol:
+ return symbol
+
+ s = str(symbol).upper()
+
+ # If a colon is present, keep the left side, strip separators, and append .P
+ if ":" in s:
+ left, _ = s.split(":", 1)
+ left_clean = left.replace("-", "").replace("/", "")
+ return f"{left_clean}.P"
+
+ # Otherwise remove common separators and return uppercase
+ return s.replace("-", "").replace("/", "")
+
+
def get_exchange_cls(exchange_id: str):
"""Get CCXT exchange class by exchange ID."""
diff --git a/python/valuecell/utils/model.py b/python/valuecell/utils/model.py
index 4e9e19bd5..a24a7c651 100644
--- a/python/valuecell/utils/model.py
+++ b/python/valuecell/utils/model.py
@@ -327,6 +327,44 @@ def create_model_with_provider(
return provider_instance.create_model(model_id, **kwargs)
+def supports_model_images(provider: str, model_id: Optional[str]) -> bool:
+ """Return True if the given provider+model_id supports image inputs.
+
+ This inspects the provider YAML loaded by `ConfigManager` and looks for a
+ `supported_inputs` entry on the matching model definition. If `model_id` is
+ None the provider's `default_model` is used. The check is case-insensitive
+ and tolerant of missing fields.
+ """
+ try:
+ from valuecell.config.manager import get_config_manager
+
+ cfg_mgr = get_config_manager()
+ prov = cfg_mgr.get_provider_config(provider)
+ if not prov:
+ return False
+
+ models = prov.models or []
+ target_id = model_id or prov.default_model
+
+ for m in models:
+ mid = m.get("id") or m.get("name") or ""
+ if not mid:
+ continue
+ if target_id and target_id != "" and mid != target_id:
+ # continue searching for exact match
+ continue
+ inputs = m.get("supported_inputs") or []
+ # normalize to lower-case
+ if any(str(i).lower() == "images" for i in inputs):
+ return True
+ return False
+ except Exception:
+ logger.debug(
+ "supports_model_images: failed to read provider config, assuming False"
+ )
+ return False
+
+
# ============================================
# Embedding Functions
# ============================================
diff --git a/python/valuecell/utils/path.py b/python/valuecell/utils/path.py
index 26a6cb045..7b90d5f44 100644
--- a/python/valuecell/utils/path.py
+++ b/python/valuecell/utils/path.py
@@ -88,3 +88,15 @@ def get_knowledge_path() -> str:
pass
return str(new_path)
+
+
+def get_screenshot_path() -> str:
+ """
+ Returns the path to the screenshots directory located in the system application directory.
+
+ Returns:
+ str: Absolute path of the screenshots directory
+ """
+ screenshot_path = Path(get_system_env_dir()) / "screenshots"
+ screenshot_path.mkdir(parents=True, exist_ok=True)
+ return str(screenshot_path)