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feat: frontend, collector e settings updates

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- mt5_collector: session start detection via gap M1, bar volume annotation, imbalance events tracking
- settings: HISTORY_FROM_DATE, HISTORY_SESSION_START flags
- FootprintCanvas: imbalance dots (centralizados, desativados por ora), stacked imbalance removido

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
rufinomec-afk
2026-06-07 16:05:59 -03:00
co-authored by Claude Sonnet 4.6
parent 3f48ef27ed
commit a2488eaab2
3 changed files with 87 additions and 39 deletions
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backend/mt5_collector.py
+57 -6
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@@ -24,6 +24,7 @@ class MT5Collector:
self.last_tick_time_msc = 0
self.seen_ticks_buffer = set()
self.last_mid_price = 0.0
self.last_bid_price = 0.0
self.last_is_buy = True
self.last_bid = 0.0
self.last_ask = 0.0
@@ -88,6 +89,41 @@ class MT5Collector:
logger.error(f"Error during MT5 shutdown: {e}")
self.connected = False
async def _find_last_session_open(self):
"""
Detecta o início da última sessão de mercado buscando o maior gap
nos últimos N bars M1. Um gap > 30min indica fechamento de sessão.
Retorna o datetime do primeiro bar após o gap (abertura de sessão).
"""
from datetime import datetime, timedelta
SESSION_GAP_MINUTES = 30
LOOKBACK_BARS = 3000 # ~50h de M1
rates = await asyncio.to_thread(
mt5.copy_rates_from_pos, self.symbol, mt5.TIMEFRAME_M1, 0, LOOKBACK_BARS
)
if rates is None or len(rates) < 2:
logger.warning("_find_last_session_open: sem bars M1, usando HISTORY_HOURS")
return datetime.now() - timedelta(hours=settings.HISTORY_HOURS)
# Percorre de trás para frente procurando o maior gap (fechamento de sessão)
best_gap = 0
session_open_ts = None
for i in range(len(rates) - 1, 0, -1):
gap_sec = int(rates[i]['time']) - int(rates[i - 1]['time'])
if gap_sec > best_gap:
best_gap = gap_sec
session_open_ts = int(rates[i]['time'])
if session_open_ts and best_gap >= SESSION_GAP_MINUTES * 60:
dt = datetime.fromtimestamp(session_open_ts)
logger.info(f"Última abertura de sessão detectada: {dt} (gap de {best_gap//60}min)")
return dt
else:
logger.warning("Nenhum gap de sessão encontrado, usando HISTORY_HOURS")
return datetime.now() - timedelta(hours=settings.HISTORY_HOURS)
async def start(self):
self.running = True
backoff = 1.0
@@ -103,9 +139,13 @@ class MT5Collector:
else:
backoff = 1.0
# Fetch from 48 hours ago so the chart isn't empty when started (covers weekends)
from datetime import datetime, timedelta
start_time_dt = datetime.now() - timedelta(hours=48)
if settings.HISTORY_FROM_DATE:
start_time_dt = datetime.strptime(settings.HISTORY_FROM_DATE, "%Y.%m.%d")
elif settings.HISTORY_SESSION_START:
start_time_dt = await self._find_last_session_open()
else:
start_time_dt = datetime.now() - timedelta(hours=settings.HISTORY_HOURS)
ticks = await asyncio.to_thread(
mt5.copy_ticks_from, self.symbol, start_time_dt, 100000, mt5.COPY_TICKS_ALL
)
@@ -165,21 +205,32 @@ class MT5Collector:
if ask_price > 0: self.last_ask = ask_price
mid_price = (bid_price + ask_price) / 2.0 if (bid_price > 0 and ask_price > 0) else 0.0
prev_mid = self.last_mid_price
prev_bid = self.last_bid_price
# Update direction tracker from mid movement
if mid_price > 0:
# Update direction tracker from bid movement (YuCluster uses Bid as price reference)
if bid_price > 0:
if bid_price > self.last_bid_price:
self.last_is_buy = True
elif bid_price < self.last_bid_price:
self.last_is_buy = False
self.last_bid_price = bid_price
elif mid_price > 0:
if mid_price > self.last_mid_price:
self.last_is_buy = True
elif mid_price < self.last_mid_price:
self.last_is_buy = False
if mid_price > 0:
self.last_mid_price = mid_price
# Determine price (use mid as best proxy for CFD quote feed)
price = last_price if last_price > 0 else (mid_price if mid_price > 0 else (bid_price if bid_price > 0 else ask_price))
# Volume = price movement in tick-size units (how the YuCluster measures activity)
# Volume = bid price movement in tick-size units (YuCluster: "Ticks & Bid")
tick_sz = self.aggregator.tick_size if self.aggregator.tick_size > 0 else 0.01
if prev_mid > 0 and mid_price > 0:
if prev_bid > 0 and bid_price > 0:
price_steps = abs(bid_price - prev_bid) / tick_sz
volume = max(price_steps, 1.0)
elif prev_mid > 0 and mid_price > 0:
price_steps = abs(mid_price - prev_mid) / tick_sz
volume = max(price_steps, 1.0)
else: