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2025.12.09 14:00
:0% :0% （30代／女性）
人気のポスト ※表示されているRP数は特定時点のものです
庄司浩平登場！ 12/26発売『週刊TVガイド新春特大号』 庄司「こういった機会があると改めて背筋が伸びる感じがありました」 – TREND NEWS CASTER https://t.co/f7Ptvp2FR7 #庄司浩平 https://t.co/XEOFl1M7XC December 12, 2025
28RP
✦ . ⁺ . ✦ . ⁺ . ✦ . ⁺ . ✦ . ⁺ ✦
LINE MUSIC 2025年間ランキング
🏆 TREND AWARDS 受賞 🏆
✦ . ⁺ . ✦ . ⁺ . ✦ . ⁺ . ✦ . ⁺ ✦
#LINEMUSIC で今年話題になった
注目の楽曲に「倍倍FIGHT!」が受賞しました！🎖️
「倍倍FIGHT!」にたくさん注目していただきありがとうございます✨
受賞を記念してもう一回みんなで「倍倍FIGHT!」聴いてくれるかな？
▼LINE MUSICで「倍倍FIGHT!」を聴く🎧
https://t.co/ZNtShRWKsB
▼Music Video
https://t.co/TDlI4irExa
@LINEMUSIC_JP
#LINEMUSICと振り返る2025
#CANDYTUNE #きゃんちゅー December 12, 2025
1RP
一番最後の[Prompt for original image]の部分に画像生成に使用したPromptを入れると一貫性が増します。不要な場合は3行削ってしまっても大丈夫です。
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Extreme wide-angle perspective and dynamic pose remix edit.
This is an EDIT of the original image, not a new character.
Use the original image as a strict reference for:
– the person’s identity, hairstyle, and overall fashion style,
– the general type of background and location (same street, same room, same beach, same kind of architecture, etc.).
You are allowed to completely change the camera position, angle, and pose, but you must keep the scene in the SAME location and keep the SAME person and outfit design.
Camera and perspective:
– Use an ultra wide-angle or fisheye feeling lens (around 12–18mm full-frame look).
– The camera angle MUST change significantly from the original: use dramatic angles such as
• worm’s-eye view from directly below looking up,
• bird’s-eye view from directly above looking down,
• very low angle from the ground,
• high angle from above,
• tilted Dutch angles.
– Always create strong foreshortening: body parts close to the lens look huge, while the rest of the body falls away in perspective.
– The final result must look like a bold fashion or street photo, fully photorealistic, not illustration or anime.
Background consistency:
– Keep the same location as the original image: same street, same bridge, same room, same studio, same beach, same general structures and materials.
– Do NOT replace the background with a completely different place.
– Because the camera angle changes, it is allowed and expected that different parts of the environment become visible.
– When new areas appear, extend the original environment logically (same buildings, fences, road markings, walls, colors, materials, lighting style), as if the camera moved within the same place.
Body parts near the lens (1–2 parts, sometimes 3):
– In each edit, choose ONE or TWO main body parts to be extremely close to the lens (sometimes even THREE in more complex poses).
– Vary them from image to image, do NOT always use the same body part.
– Allowed near-the-lens parts include:
• one or both hands / fingers reaching toward the camera,
• one or both feet / shoes / boots near the lens,
• knees or thighs,
• face very close to the lens,
• shoulders or chest close to the lens in a leaning pose.
– The chosen body parts should come extremely close to the lens, almost touching it, with visible skin texture, fabric texture, and realistic wide-angle distortion.
Pose and overall body (complex and varied):
– Create strong, cool, dynamic poses that match the extreme perspective.
– Randomly use different pose types, including:
• standing with one leg or one arm reaching toward the camera,
• crouching or squatting low to the ground,
• sitting on the floor or on objects,
• lying on the ground with legs or feet toward the lens,
• leaning forward aggressively toward the camera,
• twisting the body, crossing legs, or arching the back for more dynamic lines.
– Allow complex poses where:
• both hands are near the lens forming shapes (peace signs, triangles, frames, pointing toward the viewer),
• both feet are toward the lens,
• one hand and one foot are both large in the foreground,
• the face is close to the lens while hands or feet are also visible in perspective.
– Maintain believable anatomy even with extreme foreshortening.
Angle and attitude (randomized):
– Randomize camera angle and orientation (up, down, side, Dutch tilt) while keeping the composition visually balanced and powerful.
– Keep the vibe cool, confident, and fashion/editorial or street style, depending on the original outfit.
– Facial expressions can vary (serious, playful, confident, mysterious), but must still look like the same person.
Lighting and rendering:
– Keep the general time of day and lighting mood similar to the original (night vs day, indoor vs outdoor, soft vs hard light), but you may enhance contrast and color to make the image punchy and dramatic.
– Maintain realistic shadows and contact points with the ground or floor.
– High-resolution, sharp details with clear skin texture, fabric weave, and material highlights.
Variation and randomness:
– Each edit should look noticeably different from the original image and from other edits, with different:
• camera angles,
• pose types,
• which body parts are closest to the lens,
• orientation (straight, tilted, from above, from below).
– Avoid repeating the exact same single-foot-close-up composition; produce a wide variety of dynamic poses and angles.
Strict rules:
– Do NOT change the person into someone else.
– Do NOT change the outfit type; only restyle it through pose, perspective, and small natural movement of clothing.
– Do NOT move the scene to a completely different location; always stay in a plausible extension of the original place.
– Do NOT add text, logos, watermarks, or graphic design elements.
– Do NOT switch to painting, illustration, or anime style; keep it photorealistic.
Overall:
Transform the original photo into a dramatic, photorealistic, ultra wide-angle shot with an extreme camera angle (including views from directly below or above), where one or more body parts are right next to the lens and look huge, the rest of the body recedes in perspective, and the same person strikes a stylish, complex, powerful pose in a consistent, expanded version of the original environment.
Also, below is the prompt for generating the original image. Please use it as a reference.
[Prompt for original image]
#nanobanana2 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=n1i=0∑n−1TRt−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=maxi∈[t−W,t]HiBoxHigh=i∈[t−W,t]maxHi
BoxLow=mini∈[t−W,t]LiBoxLow=i∈[t−W,t]minLi
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_trendTconsolidation
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=⎩⎨⎧0.20.50.81.0if 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−Nt1(∣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=⎩⎨⎧1.00.70.40if 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=ATRtrendATRcurrent
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=⎩⎨⎧1.00.70.40if 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−VolumetrendVolumeconsolidation
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=⎩⎨⎧ConsolidationTrendUncertainif 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=⎩⎨⎧10if PositionRatio>0.75∧TopReversalSignal=True∧RSI>60∧Volume<Volume20×0.9otherwise
顶部反转识别：
TopReversalSignal={Trueif UpperShadowRatio>0.4∧Close<Open∧Close<EMA13FalseotherwiseTopReversalSignal=⎩⎨⎧TrueFalseif UpperShadowRatio>0.4∧Close<Open∧Close<EMA13otherwise
其中：UpperShadowRatio=Ht−max(Ot,Pt)Ht−LtUpperShadowRatio=Ht−LtHt−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(V20Vt−1,1.0)
持续性确认：
Spersistence=∑i=021(Closet−i<BoxLow)3Spersistence=3∑i=021(Closet−i<BoxLow)
有效突破判断：
ValidBreakout={Trueif BreakoutStrength>0.7∧Pt<BoxLow×0.98∧NoRecentFalseBreak=TrueFalseotherwiseValidBreakout=⎩⎨⎧TrueFalseif BreakoutStrength>0.7∧Pt<BoxLow×0.98∧NoRecentFalseBreak=Trueotherwise
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=⎩⎨⎧10if t∈PullbackWindow∧PullbackRatio∈[0.382,0.618]∧∣Pt−BoxLow∣<0.03×BoxHeight∧RejectionSignal=Trueotherwise
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
どっちのお顔も好き
#illust #illustration
#NeedyGirlOverdose #初音ミク
#trend https://t.co/1zIBPuihD1 December 12, 2025
昨晩からBSテレ東の情報番組「TREND EYE」にて、当社が紹介されています
番組内では、カンボジア不動産投資についてインタビューを受け、現地の最新動向や投資の魅力についてお話ししております
◾️番組名：BSテレ東「TREND EYE」
◾️放送日時：2025年12月8日 (月) ～ 12月12日 (金) 23:58～24:00（5日間）
※BSテレ東の「WBS ワールドビジネスサテライト」終了後に放送されます
Xのポストをシェア頂けると嬉しいです🙇♀️
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地震で被害に遭われた皆さまにお見舞い申し上げます
皆さまのご安全をお祈りしています December 12, 2025
めっちゃ買ったやつが、まさかの急上昇！うそでしょ？あのRENDER-USDがこんなに元気になるなんて信じられない！💸 https://t.co/m9c4cEmsfe December 12, 2025
見て！$RENDER-USDがまた上がってる！すごい嬉しい！初めての大勝利だよ！今は4.77%も上昇中！🪙🔥 これから楽しみ！ https://t.co/iwZI6VdbfR December 12, 2025
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