import haze_library as haze

# 价格数据
close = [100.0, 101.0, 102.0, 101.5, 103.0, 102.5, 104.0]
high = [101.0, 102.0, 103.0, 102.5, 104.0, 103.5, 105.0]
low = [99.0, 100.0, 101.0, 100.5, 102.0, 101.5, 103.0]
volume = [1000, 1200, 1100, 1300, 1250, 1150, 1400]

# 移动平均线
sma = haze.sma(close, period=5)
ema = haze.ema(close, period=5)

# 动量指标
rsi = haze.rsi(close, period=14)
macd, signal, hist = haze.macd(close, fast=12, slow=26, signal=9)

# 波动率指标
atr = haze.atr(high, low, close, period=14)
upper, middle, lower = haze.bollinger_bands(close, period=20, std_dev=2.0)

# 趋势指标
supertrend, direction = haze.supertrend(high, low, close, period=10, multiplier=3.0)
adx = haze.adx(high, low, close, period=14)

# 成交量指标
obv = haze.obv(close, volume)
vwap = haze.vwap(high, low, close, volume)

import pandas as pd
import haze_library

# 加载数据
df = pd.read_csv('ohlcv.csv')

# 使用 .haze 访问器
df['sma_20'] = df['close'].haze.sma(20)
df['rsi_14'] = df['close'].haze.rsi(14)
df['atr_14'] = df.haze.atr(14)

# 布林带（返回多列）
bb = df['close'].haze.bollinger_bands(20, 2.0)
df['bb_upper'] = bb['upper']
df['bb_middle'] = bb['middle']
df['bb_lower'] = bb['lower']

import numpy as np
from haze_library import np_ta

close = np.random.randn(1000) + 100

# 计算指标（返回 np.ndarray）
sma = np_ta.sma(close, period=20)
rsi = np_ta.rsi(close, period=14)
macd, signal, hist = np_ta.macd(close)

from haze_library.streaming import (
    IncrementalSMA,
    IncrementalRSI,
    IncrementalMACD,
    IncrementalBollingerBands,
)

# 创建流式计算器
sma = IncrementalSMA(period=20)
rsi = IncrementalRSI(period=14)
macd = IncrementalMACD(fast=12, slow=26, signal=9)

# 逐个数据点更新（O(1) 复杂度）
for price in realtime_prices:
    sma_value = sma.update(price)
    rsi_value = rsi.update(price)
    macd_line, signal_line, histogram = macd.update(price)

    print(f"SMA: {sma_value:.2f}, RSI: {rsi_value:.2f}")

import haze_library as haze

# 检测 XABCD 谐波形态
# 返回：信号(1=看涨/-1=看跌)、PRZ上沿、PRZ下沿、完成概率
signals, prz_up, prz_lo, prob = haze.harmonics(high, low, close)

# 获取详细形态信息
patterns = haze.harmonics_patterns(high, low, left_bars=5, right_bars=5)
for p in patterns:
    print(f"{p.pattern_type_zh}: {p.state}")
    print(f"  PRZ 中心: {p.prz_center:.2f}")
    print(f"  完成概率: {p.completion_probability:.1%}")

from haze_library import ml

# 特征提取
features = ml.extract_features(close, high, low, volume)

# 训练 SVM 模型
model = ml.train_svm(features, labels)

# 预测
predictions = model.predict(new_features)

import numpy as np
from haze_library import lt_indicator

# 准备价格数据（至少 500+ 个数据点以获得稳定信号）
n = 1000
high = np.array([100.0 + i * 0.1 + np.random.rand() * 2 for i in range(n)])
low = np.array([100.0 + i * 0.1 - np.random.rand() * 2 for i in range(n)])
close = np.array([100.0 + i * 0.1 for i in range(n)])
volume = np.array([1000.0 + np.random.rand() * 500 for _ in range(n)])

# 计算 LT 组合指标
result = lt_indicator(high, low, close, volume)

# 查看最终信号
print(f"交易信号: {result['ensemble']['final_signal']}")  # BUY / SELL / NEUTRAL
print(f"信号强度: {result['ensemble']['confidence']:.2%}")  # 0-100%
print(f"市场状态: {result['market_regime']}")  # TRENDING / RANGING / VOLATILE

# 查看当前市场状态
regime = result['market_regime']
print(f"市场状态: {regime}")

# 不同市场状态的权重策略
if regime == "TRENDING":
    # 趋势指标权重高 (SuperTrend, MACD, Regression)
    print("→ 适合趋势跟踪策略")
elif regime == "RANGING":
    # 均值回归指标权重高 (Pivot, Volume Profile)
    print("→ 适合区间交易策略")
elif regime == "VOLATILE":
    # 波动率指标权重高 (ATR2, Market Structure)
    print("→ 适合波动率突破策略")

# 查看所有指标的独立信号
for name, data in result['indicators'].items():
    signal = data.get('signal', 'N/A')
    confidence = data.get('confidence', 0.0)
    print(f"{name:30} -> {signal:8} ({confidence:.1%})")

# 示例输出:
# ai_supertrend               -> BUY      (85.3%)
# atr2_signals                -> BUY      (72.1%)
# ai_momentum                 -> NEUTRAL  (45.0%)
# volume_profile              -> SELL     (38.2%)
# ...

# 集成投票结果
ensemble = result['ensemble']
print(f"\n最终信号: {ensemble['final_signal']}")
print(f"多头票数: {ensemble['bullish_votes']}")
print(f"空头票数: {ensemble['bearish_votes']}")
print(f"中性票数: {ensemble['neutral_votes']}")
print(f"综合信心: {ensemble['confidence']:.2%}")

import pandas as pd
from haze_library import lt_indicator

# 加载真实市场数据
df = pd.read_csv('BTC_USDT_1h.csv')  # 至少 500+ 行数据

# 计算 LT 信号
result = lt_indicator(
    df['high'].values,
    df['low'].values,
    df['close'].values,
    df['volume'].values
)

# 获取最新信号
signal = result['ensemble']['final_signal']
confidence = result['ensemble']['confidence']
regime = result['market_regime']

# 交易逻辑
if signal == "BUY" and confidence > 0.6:
    if regime == "TRENDING":
        print("✅ 强烈看涨信号 - 开多仓 (趋势跟踪)")
    elif regime == "RANGING":
        print("✅ 看涨信号 - 区间下沿做多")
    else:
        print("⚠️  看涨信号 - 高波动期谨慎操作")

elif signal == "SELL" and confidence > 0.6:
    if regime == "TRENDING":
        print("❌ 强烈看跌信号 - 开空仓 (趋势跟踪)")
    elif regime == "RANGING":
        print("❌ 看跌信号 - 区间上沿做空")
    else:
        print("⚠️  看跌信号 - 高波动期谨慎操作")

else:
    print("⏸️  中性信号 - 观望等待更明确机会")

# 风险管理建议
if confidence < 0.4:
    print("⚠️  低信心信号 - 建议减小仓位或不交易")
elif confidence < 0.6:
    print("ℹ️  中等信心 - 标准仓位")
else:
    print("💪 高信心信号 - 可适当增加仓位（不超过最大仓位限制）")

# 检测蜡烛图形态
patterns = haze.detect_candlestick_patterns(open, high, low, close)

import haze_library as haze

try:
    # 周期过大
    rsi = haze.rsi([100, 101, 102], period=14)
except ValueError as e:
    print(f"错误: {e}")
    # 输出: Invalid period: 14 (must be > 0 and <= data length 3)

try:
    # 数组长度不匹配
    atr = haze.atr([101, 102], [99, 100], [100, 101, 102], period=2)
except ValueError as e:
    print(f"错误: {e}")
    # 输出: Length mismatch

try:
    # 空数据
    rsi = haze.rsi([], period=14)
except ValueError as e:
    print(f"错误: {e}")
    # 输出: Empty input

from haze_library.execution import ExecutionEngine, ExecutionPermissions
from haze_library.execution.providers.ccxt import CCXTProvider

# 创建交易执行引擎
provider = CCXTProvider(
    exchange="binance",
    api_key="your_key",
    api_secret="your_secret",
)

permissions = ExecutionPermissions(
    live_trading=True,
    max_notional_per_order=1000.0,  # 单笔最大 1000 USDT
)

engine = ExecutionEngine(provider=provider, permissions=permissions)

# 下单
from haze_library.execution.models import CreateOrderRequest

order_req = CreateOrderRequest(
    symbol="BTC/USDT",
    side="buy",
    order_type="limit",
    amount=0.001,
    price=50000.0,
)

order, check = engine.place_order(order_req)
print(f"订单 ID: {order.id}")