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KING SWING RENDEZ-VOUS vol.03
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@LINEMUSIC_JP
#LINEMUSICと振り返る2025
#CANDYTUNE #きゃんちゅー December 12, 2025

做了一个简化版的交易系统逻辑总结

作用：识别市场从趋势到震荡再到突破的完整生命周期，并在每个阶段采用策略。

以下是数学语言和代码👇：
（代码太长了，删了一部分，可以根据数学语言自己推）

价格序列与技术指标：

Pt=第t期收盘价Pt​=第t期收盘价
Ht,Lt,Ot,Vt=最高价、最低价、开盘价、成交量Ht​,Lt​,Ot​,Vt​=最高价、最低价、开盘价、成交量
ATRt=1n∑i=0n−1TRt−iATRt​=n1​i=0∑n−1​TRt−i​
其中：TRt=max⁡(Ht−Lt,∣Ht−Pt−1∣,∣Lt−Pt−1∣)TRt​=max(Ht​−Lt​,∣Ht​−Pt−1​∣,∣Lt​−Pt−1​∣)
箱体结构参数：
BoxHigh=max⁡i∈[t−W,t]HiBoxHigh=i∈[t−W,t]max​Hi​
BoxLow=min⁡i∈[t−W,t]LiBoxLow=i∈[t−W,t]min​Li​
BoxHeight=BoxHigh−BoxLowBoxHeight=BoxHigh−BoxLow
建议窗口期 W=30∼50W=30∼50 根K线。
相对位置函数：
PositionRatiot=Pt−BoxLowBoxHeightPositionRatiot​=BoxHeightPt​−BoxLow​
分层结构：
Leveli=BoxLow+i×BoxHeight4,i∈{0,1,2,3,4}Leveli​=BoxLow+i×4BoxHeight​,i∈{0,1,2,3,4}
多维度震荡识别系统
震荡评分函数
构建四维评分体系来量化震荡状态：
ConsolidationScore=w1⋅Stime+w2⋅Sspace+w3⋅Svolatility+w4⋅SvolumeConsolidationScore=w1​⋅Stime​+w2​⋅Sspace​+w3​⋅Svolatility​+w4​⋅Svolume​
权重建议：w1=0.35,w2=0.30,w3=0.20,w4=0.15w1​=0.35,w2​=0.30,w3​=0.20,w4​=0.15
时间维度评分
整理时间比率：
Rtime=TconsolidationTprevious_trendRtime​=Tprevious_trend​Tconsolidation​​
时间评分函数：
Stime={0.2if Rtime<0.50.5if 0.5≤Rtime<1.00.8if 1.0≤Rtime<2.01.0if Rtime≥2.0Stime​=⎩⎨⎧​0.20.50.81.0​if Rtime​<0.5if 0.5≤Rtime​<1.0if 1.0≤Rtime​<2.0if Rtime​≥2.0​
空间维度评分
边界测试频率：
TestFrequency=∑i=t−Nt1(∣Pi−BoxEdge∣<ϵ)NTestFrequency=N∑i=t−Nt​1(∣Pi​−BoxEdge∣<ϵ)​
其中 ϵ=0.02×BoxHeightϵ=0.02×BoxHeight（边界容差）
箱体强度评分：
Sspace={1.0if UpperTests≥3∧LowerTests≥3∧Breaks=00.7if TotalTests≥4∧Breaks≤10.4if TotalTests≥2∧Breaks≤20otherwiseSspace​=⎩⎨⎧​1.00.70.40​if UpperTests≥3∧LowerTests≥3∧Breaks=0if TotalTests≥4∧Breaks≤1if TotalTests≥2∧Breaks≤2otherwise​
波动率维度评分
波动率收敛指标：
VolatilityRatio=ATRcurrentATRtrendVolatilityRatio=ATRtrend​ATRcurrent​​
波动率评分：
Svolatility={1.0if VolatilityRatio<0.50.7if 0.5≤VolatilityRatio<0.70.4if 0.7≤VolatilityRatio<0.90if VolatilityRatio≥0.9Svolatility​=⎩⎨⎧​1.00.70.40​if VolatilityRatio<0.5if 0.5≤VolatilityRatio<0.7if 0.7≤VolatilityRatio<0.9if VolatilityRatio≥0.9​
成交量维度评分
量能萎缩度：
VolumeDecay=1−Volume‾consolidationVolume‾trendVolumeDecay=1−Volumetrend​Volumeconsolidation​​
成交量评分：
Svolume=min⁡(VolumeDecay×2,1.0)Svolume​=min(VolumeDecay×2,1.0)
状态判断逻辑
MarketState={Consolidationif ConsolidationScore>0.65Trendif ConsolidationScore<0.35UncertainotherwiseMarketState=⎩⎨⎧​ConsolidationTrendUncertain​if ConsolidationScore>0.65if ConsolidationScore<0.35otherwise​
三状态交易系统设计
状态机框架
定义三个核心状态：
Ω={Strend,Sconsolidation,Sbreakout}Ω={Strend​,Sconsolidation​,Sbreakout​}
状态转移条件：
Strend→SconsolidationStrend​→Sconsolidation​：
TrendStrength<0.3∧ConsolidationScore>0.5TrendStrength<0.3∧ConsolidationScore>0.5
Sconsolidation→SbreakoutSconsolidation​→Sbreakout​：
BreakoutStrength>0.7∧ValidBreakout=TrueBreakoutStrength>0.7∧ValidBreakout=True
区间交易策略（震荡状态）
做空信号生成：
ShortSignalrange={1if PositionRatio>0.75∧TopReversalSignal=True∧RSI>60∧Volume<Volume‾20×0.90otherwiseShortSignalrange​=⎩⎨⎧​10​if ​PositionRatio>0.75∧TopReversalSignal=True∧RSI>60∧Volume<Volume20​×0.9​otherwise​
顶部反转识别：
TopReversalSignal={Trueif UpperShadowRatio>0.4∧Close<Open∧Close<EMA13FalseotherwiseTopReversalSignal=⎩⎨⎧​TrueFalse​if ​UpperShadowRatio>0.4∧Close<Open∧Close<EMA13​​otherwise​
其中：UpperShadowRatio=Ht−max⁡(Ot,Pt)Ht−LtUpperShadowRatio=Ht​−Lt​Ht​−max(Ot​,Pt​)​
仓位与风控：
Positionrange=Capital×RiskRatio×0.5∣EntryPrice−StopLoss∣Positionrange​=∣EntryPrice−StopLoss∣Capital×RiskRatio×0.5​
StopLossrange=BoxHigh×(1+SafetyMargin)StopLossrange​=BoxHigh×(1+SafetyMargin)
建议 SafetyMargin=0.04∼0.05SafetyMargin=0.04∼0.05
突破跟随策略（突破状态）
突破强度评分：
BreakoutStrength=0.4⋅Sprice+0.3⋅Svolume+0.3⋅SpersistenceBreakoutStrength=0.4⋅Sprice​+0.3⋅Svolume​+0.3⋅Spersistence​
各分量计算：
价格突破深度：
Sprice=min⁡(∣BoxLow−Pt∣BoxLow×10,1.0)Sprice​=min(BoxLow∣BoxLow−Pt​∣​×10,1.0)
成交量放大度：
Svolume=min⁡(VtV‾20−1,1.0)Svolume​=min(V20​Vt​​−1,1.0)
持续性确认：
Spersistence=∑i=021(Closet−i<BoxLow)3Spersistence​=3∑i=02​1(Closet−i​<BoxLow)​
有效突破判断：
ValidBreakout={Trueif BreakoutStrength>0.7∧Pt<BoxLow×0.98∧NoRecentFalseBreak=TrueFalseotherwiseValidBreakout=⎩⎨⎧​TrueFalse​if ​BreakoutStrength>0.7∧Pt​<BoxLow×0.98∧NoRecentFalseBreak=True​otherwise​
回踩机会识别
回踩窗口定义：
PullbackWindow=[tbreakout,tbreakout+MaxWaitBars]PullbackWindow=[tbreakout​,tbreakout​+MaxWaitBars]
建议 MaxWaitBars=20MaxWaitBars=20
回踩位置评估：
PullbackRatio=Pt−LowestAfterBreakBoxLow−LowestAfterBreakPullbackRatio=BoxLow−LowestAfterBreakPt​−LowestAfterBreak​
理想回踩区间：0.382≤PullbackRatio≤0.6180.382≤PullbackRatio≤0.618
回踩做空信号：
ShortSignalpullback={1if t∈PullbackWindow∧PullbackRatio∈[0.382,0.618]∧∣Pt−BoxLow∣<0.03×BoxHeight∧RejectionSignal=True0otherwiseShortSignalpullback​=⎩⎨⎧​10​if ​t∈PullbackWindow∧PullbackRatio∈[0.382,0.618]∧∣Pt​−BoxLow∣<0.03×BoxHeight∧RejectionSignal=True​otherwise​
动态资金分配模型
自适应分配函数
αt=αbase+Δα⋅f(ConsolidationScoret)αt​=αbase​+Δα⋅f(ConsolidationScoret​)
基础分配：
αbase=0.75（保守策略基础占比）αbase​=0.75（保守策略基础占比）
动态调整：
Δα=0.2×(1−ConsolidationScoret)Δα=0.2×(1−ConsolidationScoret​)
约束条件：0.6≤αt≤0.90.6≤αt​≤0.9
资金分配：
Capitalrange=TotalCapital×αtCapitalrange​=TotalCapital×αt​
Capitalbreakout=TotalCapital×(1−αt)Capitalbreakout​=TotalCapital×(1−αt​)

代码：
import numpy as np import pandas as pd from dataclasses import dataclass from typing import Optional, Tuple, Dict, List from enum import Enum class MarketState(Enum): TREND_DOWN = "TREND_DOWN" CONSOLIDATION = "CONSOLIDATION" BREAKOUT = "BREAKOUT" UNCERTAIN = "UNCERTAIN" @dataclass class ConsolidationMetrics: """震荡评估指标""" time_score: float space_score: float volatility_score: float volume_score: float total_score: float market_state: MarketState @dataclass class BoxStructure: """箱体结构""" high: float low: float height: float level_075: float level_050: float level_025: float upper_tests: int lower_tests: int duration: int is_valid: bool @classmethod def from_prices(cls, highs: np.ndarray, lows: np.ndarray, prices: np.ndarray, window: int = 50): """从价格数据构建箱体""" box_high = np.max(highs[-window:]) box_low = np.min(lows[-window:]) box_height = box_high - box_low # 计算关键位置 level_075 = box_low + 0.75 * box_height level_050 = box_low + 0.50 * box_height level_025 = box_low + 0.25 * box_height # 计算边界测试次数 epsilon = 0.02 * box_height upper_tests = np.sum(np.abs(prices - box_high) < epsilon) lower_tests = np.sum(np.abs(prices - box_low) < epsilon) # 验证有效性 is_valid = ( box_height / box_low > 0.15 and upper_tests >= 2 and lower_tests >= 2 and len(prices) >= 20 ) return cls( high=box_high, low=box_low, height=box_height, level_075=level_075, level_050=level_050, level_025=level_025, upper_tests=upper_tests, lower_tests=lower_tests, duration=len(prices), is_valid=is_valid ) def get_position_ratio(self, price: float) -> float: """计算价格在箱体中的相对位置""" if self.height == 0: return 0.5 return (price - self.low) / self.height class ConsolidationBreakoutStrategy: """震荡识别与突破策略""" def __init__(self, capital: float = 100000, risk_per_trade: float = 0.02, lookback_period: int = 50): """ 初始化策略 Args: capital: 总资金 risk_per_trade: 单笔风险比例 lookback_period: 回溯周期 """ https://t.co/CclsZvqogq = capital self.risk_per_trade = risk_per_trade self.lookback_period = lookback_period # 核心参数 self.params = { # 震荡识别 'consolidation_threshold': 0.65, 'trend_threshold': 0.35, 'time_weight': 0.35, 'space_weight': 0.30, 'volatility_weight': 0.20, 'volume_weight': 0.15, # 区间交易 'top_threshold': 0.75, 'range_position_ratio': 0.5, 'safety_margin': 0.04, # 突破跟随 'breakout_threshold': 0.7, 'price_offset': 0.02, 'volume_multiplier': 1.5, 'persistence_bars': 3, # 回踩交易 'pullback_max_wait': 20, 'fib_low': 0.382, 'fib_high': 0.618, 'proximity_threshold': 0.03, } # 状态跟踪 self.current_state = MarketState.UNCERTAIN https://t.co/Hl1CpkP62f_structure: Optional[BoxStructure] = None self.trend_start_idx = None self.consolidation_start_idx = None self.breakout_info = None self.alpha = 0.75 # 保守策略资金占比 def calculate_technical_indicators(self, df: pd.DataFrame) -> pd.DataFrame: """计算技术指标""" df['EMA13'] = df['close'].ewm(span=13).mean() df['RSI'] = self._calculate_rsi(df['close'], 14) df['ATR'] = self._calculate_atr(df, 14) df['Volume_MA'] = df['volume'].rolling(20).mean() return df def _calculate_rsi(self, prices: pd.Series, period: int = 14) -> pd.Series: """计算RSI指标""" delta = prices.diff() gain = (delta.where(delta > 0, 0)).rolling(window=period).mean() loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean() rs = gain / loss rsi = 100 - (100 / (1 + rs)) return rsi def _calculate_atr(self, df: pd.DataFrame, period: int) -> pd.Series: """计算ATR指标""" high_low = df['high'] - df['low'] high_close = np.abs(df['high'] - df['close'].shift()) low_close = np.abs(df['low'] - df['close'].shift()) tr = np.maximum(high_low, np.maximum(high_close, low_close)) return tr.rolling(period).mean() def evaluate_consolidation(self, df: pd.DataFrame, current_idx: int) -> ConsolidationMetrics: """评估震荡状态""" if current_idx < self.lookback_period: return ConsolidationMetrics(0, 0, 0, 0, 0, MarketState.UNCERTAIN) # 1. 时间维度评分 time_score = self._calculate_time_score(current_idx) # 2. 空间维度评分 space_score = self._calculate_space_score() # 3. 波动率维度评分 volatility_score = self._calculate_volatility_score(df, current_idx) # 4. 成交量维度评分 volume_score = self._calculate_volume_score(df, current_idx) # 综合评分 total_score = ( self.params['time_weight'] * time_score + self.params['space_weight'] * space_score + self.params['volatility_weight'] * volatility_score + self.params['volume_weight'] * volume_score ) # 状态判断 if total_score > self.params['consolidation_threshold']: market_state = MarketState.CONSOLIDATION elif total_score < self.params['trend_threshold']: market_state = MarketState.TREND_DOWN else: market_state = MarketState.UNCERTAIN return ConsolidationMetrics( time_score=time_score, space_score=space_score, volatility_score=volatility_score, volume_score=volume_score, total_score=total_score, market_state=market_state ) def _calculate_time_score(self, current_idx: int) -> float: """计算时间维度评分""" if not self.trend_start_idx or not self.consolidation_start_idx: return 0.2 trend_duration = self.consolidation_start_idx - self.trend_start_idx consolidation_duration = current_idx - self.consolidation_start_idx if trend_duration == 0: return 0.2 time_ratio = consolidation_duration / trend_duration if time_ratio < 0.5: return 0.2 elif time_ratio < 1.0: return 0.5 elif time_ratio < 2.0: return 0.8 else: return 1.0 def _calculate_space_score(self) -> float: """计算空间维度评分""" if not https://t.co/Hl1CpkP62f_structure or not https://t.co/Hl1CpkP62f_structure.is_valid: return 0.0 # 边界测试充分性 total_tests = https://t.co/Hl1CpkP62f_structure.upper_tests + https://t.co/Hl1CpkP62f_structure.lower_tests test_score = min(total_tests / 6, 1.0) # 箱体持续时间 duration_score = min(https://t.co/Hl1CpkP62f_structure.duration / 30, 1.0) return (test_score + duration_score) / 2 def _calculate_volatility_score(self, df: pd.DataFrame, current_idx: int) -> float: """计算波动率维度评分""" if current_idx < 40: return 0.0 # 当前波动率 current_atr = df.iloc[current_idx]['ATR'] # 历史波动率 if self.trend_start_idx and self.consolidation_start_idx: trend_atr = df.iloc[self.trend_start_idx:self.consolidation_start_idx]['ATR'].mean() else: trend_atr = df.iloc[current_idx-40:current_idx-20]['ATR'].mean() if trend_atr == 0: return 0.0 vol_ratio = current_atr / trend_atr if vol_ratio < 0.5: return 1.0 elif vol_ratio < 0.7: return 0.7 elif vol_ratio < 0.9: return 0.4 else: return 0.0 def _calculate_volume_score(self, df: pd.DataFrame, current_idx: int) -> float: """计算成交量维度评分""" if current_idx < 40 or not self.consolidation_start_idx: return 0.0 # 震荡期平均成交量 consolidation_vol = df.iloc[self.consolidation_start_idx:current_idx]['volume'].mean() # 趋势期平均成交量 if self.trend_start_idx: trend_vol = df.iloc[self.trend_start_idx:self.consolidation_start_idx]['volume'].mean() else: trend_vol = df.iloc[current_idx-40:current_idx-20]['volume'].mean() if trend_vol == 0: return 0.0 volume_decay = 1 - (consolidation_vol / trend_vol) return min(volume_decay * 2, 1.0) def update_box_structure(self, df: pd.DataFrame, current_idx: int): """更新箱体结构""" if current_idx < self.lookback_period: return window_data = df.iloc[current_idx-self.lookback_period:current_idx+1] https://t.co/Hl1CpkP62f_structure = BoxStructure.from_prices( window_data['high'].values, window_data['low'].values, window_data['close'].values, window=self.lookback_period ) def detect_top_reversal(self, df: pd.DataFrame, current_idx: int) -> bool: """检测顶部反转信号""" if current_idx < 1: return False current = df.iloc[current_idx] # 上影线比例 range_size = current['high'] - current['low'] if range_size == 0: return False upper_shadow = current['high'] - max(current['open'], current['close']) upper_shadow_ratio = upper_shadow / range_size # 综合条件 is_bearish = current['close'] < current['open'] below_ema = current['close'] < current['EMA13'] return upper_shadow_ratio > 0.4 and is_bearish and below_ema def generate_range_signal(self, df: pd.DataFrame, current_idx: int) -> Optional[Dict]: """生成区间交易信号""" if not https://t.co/Hl1CpkP62f_structure or not https://t.co/Hl1CpkP62f_structure.is_valid: return None current_price = df.iloc[current_idx]['close'] position_ratio = https://t.co/Hl1CpkP62f_structure.get_position_ratio(current_price) # 必须在顶部区域 if position_ratio < self.params['top_threshold']: return None # 检测反转信号 if not self.detect_top_reversal(df, current_idx): return None # RSI条件 rsi = df.iloc[current_idx]['RSI'] if rsi < 60: return None # 成交量条件 volume_ratio = df.iloc[current_idx]['volume'] / df.iloc[current_idx]['Volume_MA'] if volume_ratio > 0.9: return None # 计算交易参数 stop_loss = https://t.co/Hl1CpkP62f_structure.high * (1 + self.params['safety_margin']) position_size = self._calculate_position_size(current_price, stop_loss, 0.5) return { 'signal_type': 'RANGE_SHORT', 'entry_price': current_price, 'stop_loss': stop_loss, 'target1': https://t.co/Hl1CpkP62f_structure.level_050, 'target2': https://t.co/Hl1CpkP62f_structure.level_025, 'position_size': position_size, 'position_ratio': position_ratio, 'reason': f'区间顶部做空 (位置={position_ratio:.2%})' } def calculate_breakout_strength(self, df: pd.DataFrame, current_idx: int) -> float: """计算突破强度""" if not https://t.co/Hl1CpkP62f_structure or current_idx < 20: return 0.0 current_price = df.iloc[current_idx]['close'] # 价格突破深度 price_penetration = abs(https://t.co/Hl1CpkP62f_structure.low - current_price) / https://t.co/Hl1CpkP62f_structure.low price_score = min(price_penetration * 10, 1.0) # 成交量放大 vol_ratio = df.iloc[current_idx]['volume'] / df.iloc[current_idx]['Volume_MA'] volume_score = min(vol_ratio - 1, 1.0) # 持续性确认 persistence_count = 0 for i in range(min(self.params['persistence_bars'], current_idx)): if df.iloc[current_idx - i]['close'] < https://t.co/Hl1CpkP62f_structure.low: persistence_count += 1 else: break persistence_score = persistence_count / self.params['persistence_bars'] # 综合评分 return 0.4 * price_score + 0.3 * volume_score + 0.3 * persistence_score def generate_breakout_signal(self, df: pd.DataFrame, current_idx: int) -> Optional[Dict]: """生成突破信号""" if not https://t.co/Hl1CpkP62f_structure: return None current_price = df.iloc[current_idx]['close'] # 检查突破条件 price_break = current_price < https://t.co/Hl1CpkP62f_structure.low * (1 - self.params['price_offset']) strength = self.calculate_breakout_strength(df, current_idx) strong_enough = strength > self.params['breakout_threshold'] if not (price_break and strong_enough): return None # 记录突破信息 self.breakout_info = { 'timestamp': current_idx, 'price': current_price, 'lowest_after': current_price, 'strength': strength } # 生成信号 stop_loss = https://t.co/Hl1CpkP62f_structure.low * 1.02 position_size = self._calculate_position_size(current_price, stop_loss, 1.0) target = https://t.co/Hl1CpkP62f_structure.low - https://t.co/Hl1CpkP62f_structure.height * 2.0 return { 'signal_type': 'BREAKOUT_SHORT', 'entry_price': current_price, 'stop_loss': stop_loss, 'target1': target, 'target2': target * 0.9, 'position_size': position_size, 'breakout_strength': strength, 'reason': f'突破追空 (强度={strength:.2f})' } def generate_pullback_signal(self, df: pd.DataFrame, current_idx: int) -> Optional[Dict]: """生成回踩信号""" if not self.breakout_info: return None # 检查时间窗口 bars_since_breakout = current_idx - self.breakout_info['timestamp'] if bars_since_breakout > self.params['pullback_max_wait']: return None current_price = df.iloc[current_idx]['close'] # 计算回踩比例 lowest_after = self.breakout_info['lowest_after'] if https://t.co/Hl1CpkP62f_structure.low == lowest_after: return None pullback_ratio = (current_price - lowest_after) / (https://t.co/Hl1CpkP62f_structure.low - lowest_after) # 检查回踩区间 in_fib_range = (self.params['fib_low'] <= pullback_ratio <= self.params['fib_high']) near_resistance = (abs(current_price - https://t.co/Hl1CpkP62f_structure.low) < self.params['proximity_threshold'] * https://t.co/Hl1CpkP62f_structure.height) if not (in_fib_range and near_resistance): return None # 检测阻力受阻 current = df.iloc[current_idx] range_size = current['high'] - current['low'] if range_size > 0: upper_shadow = current['high'] - max(current['open'], current['close']) rejection = (upper_shadow / range_size > 0.4 and current['close'] < https://t.co/Hl1CpkP62f_structure.low) else: rejection = False if not rejection: return None # 生成信号 stop_loss = https://t.co/Hl1CpkP62f_structure.low * 1.015 position_size = self._calculate_position_size(current_price, stop_loss, 0.8) target = https://t.co/Hl1CpkP62f_structure.low - https://t.co/Hl1CpkP62f_structure.height * 1.5 return { 'signal_type': 'PULLBACK_SHORT', 'entry_price': current_price, 'stop_loss': stop_loss, 'target1': target, 'target2': target * 0.95, 'position_size': position_size, 'pullback_ratio': pullback_ratio, 'reason': f'回踩做空 (回踩={pullback_ratio:.2%})' } def _calculate_position_size(self, entry_price: float, stop_loss: float, multiplier: float = 1.0) -> float: """计算仓位大小""" risk_amount = https://t.co/CclsZvqogq * self.risk_per_trade stop_distance = abs(entry_price - stop_loss) if stop_distance == 0: return 0 base_position = risk_amount / stop_distance adjusted_position = base_position * multiplier # 仓位上限 max_position = (https://t.co/CclsZvqogq * 0.3) / entry_price return min(adjusted_position, max_position) def update_capital_allocation(self, consolidation_score: float): """更新资金分配""" self.alpha = 0.75 + 0.15 * (1 - consolidation_score) self.alpha = np.clip(self.alpha, 0.6, 0.9) def backtest(self, df: pd.DataFrame) -> Dict: """策略回测""" # 计算技术指标 df = self.calculate_technical_indicators(df) signals = [] state_history = [] for i in range(self.lookback_period, len(df)): # 更新箱体结构 self.update_box_structure(df, i) # 评估震荡状态 metrics = self.evaluate_consolidation(df, i) self.current_state = https://t.co/aYrw6Qe9xL_state # 记录状态 state_history.append({ 'timestamp': df.iloc[i].get('timestamp', i), 'price': df.iloc[i]['close'], 'state': self.current_state.value, 'consolidation_score': https://t.co/bneIHJbMXx_score, 'box_valid': https://t.co/Hl1CpkP62f_structure.is_valid if https://t.co/Hl1CpkP62f_structure else False }) # 更新资金分配 self.update_capital_allocation(https://t.co/bneIHJbMXx_score) # 生成交易信号 signal = None if self.current_state == MarketState.CONSOLIDATION: signal = self.generate_range_signal(df, i) elif self.current_state == MarketState.TREND_DOWN: signal = self.generate_breakout_signal(df, i) # 检查回踩机会 if not signal: signal = self.generate_pullback_signal(df, i) if signal: signal['timestamp'] = df.iloc[i].get('timestamp', i) signal['current_price'] = df.iloc[i]['close'] signal['market_state'] = self.current_state.value signal['alpha'] = self.alpha signals.append(signal) December 12, 2025

KING SWING RENDEZ-VOUS vol.03
2025.12.30 at harevutai
▶︎▷▶︎チケット先着販売中◀︎◁◀︎

▶︎KS 会員 Ticket◀︎
https://t.co/vEzx7inXMG

▶︎一般 Ticket◀︎
https://t.co/LXDHUCU659

KS会員限定イベント 『KING SWING RENDEZ-VOUS』の第3回目が12月30日（火）に東京池袋で開催！2025年の締めくくりに、ファン同士がリアルな空間で共鳴し合う、“RENDEZ-VOUS”なひとときをお楽しみください。

「KING SWING会員限定」とご案内しておりますが、より多くの人に体験していただく機会を提供したいため一部枠を限定解除して一般販売いたします。

詳細はこちら！
https://t.co/UuYc4FxK8s December 12, 2025

私のqiita記事
・Project Settingsを上から見ていきつつ自分の言葉で書いてみる　Burst AOT Settings編
・Unity6.3に上げたら自作のRendererFeatureが効かなくなった件の対策
を取り上げて下さっています

有難うございます～ https://t.co/mFTi6qnhyx December 12, 2025

❄️SEONGSU TREND COLLECTOR -gift edition- in SENDAI❄️

韓国の人気ブランドが参加しているこちらのイベントが大好評開催中🎊

参加してくださっているブランドさんをご紹介します‼️本日は、第一弾✨

① KODAK Apparel
アメリカの老舗カメラブランド「KODAK（コダック）」が展開するアパレルライン。レトロなフィルムカルチャーを現代のストリートファッションに融合させたアイテムを手掛けており、ジェンダーレスに愛されているブランド😍

② TÉTO
タオルを生活用品から小さなアートピースとして楽しめる存在へと変化させたブランド。世界でわずか1%しか生産されないハイエンド素材「スーピマコットン」を使用したTÉTOのタオルは、柔らかな肌触りと鮮やかな発色が特徴。唯一無二のカラーとデザインで、新たなタオル体験を提供します👏

③ MUUT
2024年に誕生した韓国のユニセックスなアイウェアブランド。#SEVENTEEN、#TWICE、#TXT など、多くのトップアーティストたちが着用。彼らが来店したことでファッショニスタの間でも話題になりました。ミニマルでおしゃれなデザインは、日常のスタイルを格上げし、トレンドに敏感な人々に愛されています🕶

ぜひ、お買い物の参考にしてください🛍️

◆実施期間：2025年12月5日(金)～14日(日)
◆開館時間：10:00〜20:00（最終日のみ18時閉場）
◆会場：仙台PARCO1/6F SPACE6（宮城県仙台市青葉区中央1-2-3）

#SEONGSU_TC #仙台 #仙台PARCO
#韓国 #韓国トレンド #韓国コスメ #韓国ビューティー #韓国ファッション #韓国雑貨 December 12, 2025

わー、このヘレンドの透かし花柄ボンボニエール、チョコ入れにぴったりすぎて欲しくなるよぉ^ ^ わたしも買っちゃおうかな、きゃぴっ！

ヘレンド HEREND 透かしミニボンボン ボンボニエール
https://t.co/RtYpxXz3R8 December 12, 2025

ほぼ建値
で終了

相場とは
適度な距離感
が必須

家族との
時間を優先し
筋トレし
沢山食べて寝て

トレードには
のめり込み過ぎない

これを実現
させてくれたのが

TrendStream
3StepTrade

fx初心者 ドル円 ゴールド usdjpy gbp eur aud gold xauusd 日経225 相関性 デイトレ シグナル EA 自動売買 https://t.co/9nrRxpJbI1 https://t.co/gzed1UKGoA December 12, 2025