市场情绪分析是量化交易中的重要分支，通过分析各类文本数据（如新闻、社交媒体、研报等）来捕捉市场参与者的情绪变化，进而预测市场走势。本项目旨在帮助学习者构建一个完整的基于市场情绪分析的量化交易策略，覆盖数据采集、文本处理、情绪分析、策略设计与回测优化等环节。
完成本项目后，学习者将能够：
import requests
import pandas as pd
from datetime import datetime, timedelta
import time
import os
from dotenv import load_dotenv
load_dotenv() # 加载环境变量
class DataCollector:
def __init__(self):
# 各API密钥
self.news_api_key = os.getenv("NEWS_API_KEY")
self.twitter_bearer_token = os.getenv("TWITTER_BEARER_TOKEN")
self.reddit_client_id = os.getenv("REDDIT_CLIENT_ID")
self.reddit_client_secret = os.getenv("REDDIT_CLIENT_SECRET")
def get_financial_news(self, keywords, start_date, end_date, max_results=100):
"""
从News API获取金融新闻
参数:
keywords (str): 搜索关键词，如"Bitcoin,Ethereum"
start_date (str): 开始日期，格式 YYYY-MM-DD
end_date (str): 结束日期，格式 YYYY-MM-DD
max_results (int): 最大返回结果数
返回:
pd.DataFrame: 包含新闻数据的DataFrame
"""
url = "https://newsapi.org/v2/everything"
params = {
'q': keywords,
'from': start_date,
'to': end_date,
'language': 'en',
'sortBy': 'publishedAt',
'pageSize': min(max_results, 100), # API限制
'apiKey': self.news_api_key
}
response = requests.get(url, params=params)
if response.status_code != 200:
print(f"Error: {response.status_code}, {response.text}")
return pd.DataFrame()
articles = response.json().get('articles', [])
# 创建DataFrame
if not articles:
return pd.DataFrame()
df = pd.DataFrame(articles)
df['publishedAt'] = pd.to_datetime(df['publishedAt'])
# 提取重要字段
df = df[['title', 'description', 'content', 'publishedAt', 'source', 'url']]
return df
def get_twitter_data(self, query, start_time, end_time, max_results=100):
"""
从Twitter API v2获取推文数据
参数:
query (str): 搜索查询，如"#Bitcoin"
start_time (str): 开始时间，格式 YYYY-MM-DDTHH:MM:SSZ
end_time (str): 结束时间，格式 YYYY-MM-DDTHH:MM:SSZ
max_results (int): 最大返回结果数
返回:
pd.DataFrame: 包含推文数据的DataFrame
"""
url = "https://api.twitter.com/2/tweets/search/recent"
headers = {
"Authorization": f"Bearer {self.twitter_bearer_token}"
}
params = {
'query': query,
'start_time': start_time,
'end_time': end_time,
'max_results': min(max_results, 100), # API限制
'tweet.fields': 'created_at,public_metrics,lang'
}
response = requests.get(url, headers=headers, params=params)
if response.status_code != 200:
print(f"Error: {response.status_code}, {response.text}")
return pd.DataFrame()
tweets = response.json().get('data', [])
# 创建DataFrame
if not tweets:
return pd.DataFrame()
df = pd.DataFrame(tweets)
df['created_at'] = pd.to_datetime(df['created_at'])
# 展开public_metrics
metrics_df = pd.json_normalize(df['public_metrics'])
df = pd.concat([df.drop('public_metrics', axis=1), metrics_df], axis=1)
return df
def get_reddit_data(self, subreddit, time_filter='week', limit=100):
"""
从Reddit API获取帖子数据
参数:
subreddit (str): 子版块名称，如"Bitcoin"
time_filter (str): 时间过滤器，如'day', 'week', 'month'
limit (int): 最大返回结果数
返回:
pd.DataFrame: 包含Reddit帖子数据的DataFrame
"""
# 获取认证token
auth = requests.auth.HTTPBasicAuth(self.reddit_client_id, self.reddit_client_secret)
data = {
'grant_type': 'password',
'username': os.getenv("REDDIT_USERNAME"),
'password': os.getenv("REDDIT_PASSWORD")
}
headers = {'User-Agent': 'FinanceApp/0.1'}
response = requests.post(
'https://www.reddit.com/api/v1/access_token',
auth=auth,
data=data,
headers=headers
)
if response.status_code != 200:
print(f"Authentication Error: {response.status_code}, {response.text}")
return pd.DataFrame()
token = response.json()['access_token']
headers['Authorization'] = f'bearer {token}'
# 获取帖子数据
url = f"https://oauth.reddit.com/r/{subreddit}/top"
params = {
't': time_filter,
'limit': min(limit, 100) # API限制
}
response = requests.get(url, headers=headers, params=params)
if response.status_code != 200:
print(f"Error: {response.status_code}, {response.text}")
return pd.DataFrame()
posts = response.json()['data']['children']
# 创建DataFrame
if not posts:
return pd.DataFrame()
data = []
for post in posts:
post_data = post['data']
data.append({
'title': post_data.get('title'),
'selftext': post_data.get('selftext'),
'created_utc': datetime.fromtimestamp(post_data.get('created_utc')),
'score': post_data.get('score'),
'num_comments': post_data.get('num_comments'),
'upvote_ratio': post_data.get('upvote_ratio'),
'permalink': post_data.get('permalink')
})
df = pd.DataFrame(data)
return df
def save_data(self, df, filename):
"""保存数据到CSV文件"""
df.to_csv(filename, index=False)
print(f"Data saved to {filename}")
# 使用示例
if __name__ == "__main__":
collector = DataCollector()
# 获取比特币相关新闻
end_date = datetime.now().strftime('%Y-%m-%d')
start_date = (datetime.now() - timedelta(days=7)).strftime('%Y-%m-%d')
news_df = collector.get_financial_news("Bitcoin OR Cryptocurrency", start_date, end_date)
collector.save_data(news_df, "bitcoin_news.csv")
# 注意：以下代码需要有对应的API密钥才能执行
# Twitter数据示例
# twitter_df = collector.get_twitter_data("#Bitcoin",
# (datetime.now() - timedelta(days=7)).strftime('%Y-%m-%dT%H:%M:%SZ'),
# datetime.now().strftime('%Y-%m-%dT%H:%M:%SZ'))
# collector.save_data(twitter_df, "bitcoin_tweets.csv")
# Reddit数据示例
# reddit_df = collector.get_reddit_data("Bitcoin", 'week')
# collector.save_data(reddit_df, "bitcoin_reddit.csv")
import pandas as pd
import numpy as np
import re
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
import spacy
import textblob
from textblob import TextBlob
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import torch
# 下载必要的NLTK数据
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
class TextProcessor:
def __init__(self, use_spacy=False, use_transformers=False):
"""
初始化文本处理器
参数:
use_spacy (bool): 是否使用spaCy进行高级NLP处理
use_transformers (bool): 是否使用Transformers模型进行情感分析
"""
self.lemmatizer = WordNetLemmatizer()
self.stop_words = set(stopwords.words('english'))
self.vader_analyzer = SentimentIntensityAnalyzer()
# 添加金融领域的特定停用词
finance_stopwords = {'nasdaq', 'nyse', 'market', 'stock', 'share', 'price',
'index', 'exchange', 'trading', 'trader', 'investor'}
self.stop_words.update(finance_stopwords)
# 加载spaCy模型（可选）
self.use_spacy = use_spacy
if use_spacy:
try:
self.nlp = spacy.load("en_core_web_sm")
except:
print("spaCy模型未找到。请运行: python -m spacy download en_core_web_sm")
self.use_spacy = False
# 加载transformers模型（可选）
self.use_transformers = use_transformers
if use_transformers:
try:
model_name = "ProsusAI/finbert"
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
self.finbert_pipeline = pipeline("sentiment-analysis", model=self.model, tokenizer=self.tokenizer)
except:
print("无法加载FinBERT模型。请检查您的互联网连接和transformers库安装。")
self.use_transformers = False
def clean_text(self, text):
"""
清洗文本数据
参数:
text (str): 输入文本
返回:
str: 清洗后的文本
"""
if not isinstance(text, str):
return ""
# 转换为小写
text = text.lower()
# 移除URL
text = re.sub(r'http\S+', '', text)
# 移除@用户名
text = re.sub(r'@\w+', '', text)
# 移除标签符号
text = re.sub(r'#', '', text)
# 移除特殊字符和数字
text = re.sub(r'[^a-zA-Z\s]', '', text)
# 移除多余空格
text = re.sub(r'\s+', ' ', text).strip()
return text
def tokenize_text(self, text):
"""
文本分词
参数:
text (str): 输入文本
返回:
list: 分词后的token列表
"""
tokens = word_tokenize(text)
# 移除停用词
tokens = [token for token in tokens if token not in self.stop_words]
# 词形还原
tokens = [self.lemmatizer.lemmatize(token) for token in tokens]
return tokens
def extract_entities(self, text):
"""
使用spaCy提取命名实体
参数:
text (str): 输入文本
返回:
list: 提取的实体列表
"""
if not self.use_spacy:
return []
doc = self.nlp(text)
entities = [(ent.text, ent.label_) for ent in doc.ents]
return entities
def get_textblob_sentiment(self, text):
"""
使用TextBlob计算情感分数
参数:
text (str): 输入文本
返回:
tuple: (极性, 主观性)
"""
blob = TextBlob(text)
return blob.sentiment.polarity, blob.sentiment.subjectivity
def get_vader_sentiment(self, text):
"""
使用VADER计算情感分数
参数:
text (str): 输入文本
返回:
dict: 包含pos, neg, neu和compound分数的字典
"""
return self.vader_analyzer.polarity_scores(text)
def get_finbert_sentiment(self, text):
"""
使用FinBERT计算金融特定情感
参数:
text (str): 输入文本
返回:
dict: 情感分析结果
"""
if not self.use_transformers:
return {'label': 'neutral', 'score': 0.5}
# 限制文本长度
max_length = 512
if len(text) > max_length:
text = text[:max_length]
try:
result = self.finbert_pipeline(text)[0]
return result
except Exception as e:
print(f"FinBERT分析错误: {e}")
return {'label': 'neutral', 'score': 0.5}
def process_dataframe(self, df, text_column, new_column_prefix='processed'):
"""
处理DataFrame中的文本列
参数:
df (pd.DataFrame): 输入数据框
text_column (str): 包含文本的列名
new_column_prefix (str): 新列的前缀
返回:
pd.DataFrame: 处理后的数据框
"""
# 创建DataFrame的副本
result_df = df.copy()
# 清洗文本
result_df[f'{new_column_prefix}_text'] = result_df[text_column].apply(self.clean_text)
# 分词并计算token长度
result_df[f'{new_column_prefix}_tokens'] = result_df[f'{new_column_prefix}_text'].apply(self.tokenize_text)
result_df[f'{new_column_prefix}_token_count'] = result_df[f'{new_column_prefix}_tokens'].apply(len)
# TextBlob情感分析
sentiment_results = result_df[f'{new_column_prefix}_text'].apply(self.get_textblob_sentiment)
result_df[f'{new_column_prefix}_polarity'] = sentiment_results.apply(lambda x: x[0])
result_df[f'{new_column_prefix}_subjectivity'] = sentiment_results.apply(lambda x: x[1])
# VADER情感分析
vader_results = result_df[f'{new_column_prefix}_text'].apply(self.get_vader_sentiment)
result_df[f'{new_column_prefix}_vader_neg'] = vader_results.apply(lambda x: x['neg'])
result_df[f'{new_column_prefix}_vader_neu'] = vader_results.apply(lambda x: x['neu'])
result_df[f'{new_column_prefix}_vader_pos'] = vader_results.apply(lambda x: x['pos'])
result_df[f'{new_column_prefix}_vader_compound'] = vader_results.apply(lambda x: x['compound'])
# 如果启用，添加FinBERT情感分析
if self.use_transformers:
finbert_results = result_df[f'{new_column_prefix}_text'].apply(self.get_finbert_sentiment)
result_df[f'{new_column_prefix}_finbert_label'] = finbert_results.apply(lambda x: x['label'])
result_df[f'{new_column_prefix}_finbert_score'] = finbert_results.apply(lambda x: x['score'])
# 如果启用，添加命名实体识别
if self.use_spacy:
result_df[f'{new_column_prefix}_entities'] = result_df[f'{new_column_prefix}_text'].apply(self.extract_entities)
return result_df
# 使用示例
if __name__ == "__main__":
# 创建示例数据
data = {
'date': pd.date_range(start='2023-01-01', periods=3),
'headline': [
"Bitcoin surges to new all-time high as institutional investors pile in",
"Markets crash as inflation fears grow and central bank hints at rate hikes",
"Tech stocks rally despite mixed earnings reports from FAANG companies"
]
}
df = pd.DataFrame(data)
# 初始化文本处理器（简单版本，不使用spaCy和transformers以避免依赖问题）
processor = TextProcessor(use_spacy=False, use_transformers=False)
# 处理文本
processed_df = processor.process_dataframe(df, 'headline')
# 显示结果
print(processed_df.head())
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import statsmodels.api as sm
class SentimentIndicator:
def calculate_daily_sentiment(self, df, date_column, sentiment_column, agg_method='mean'):
"""计算每日情绪得分"""
df = df.copy()
df[date_column] = pd.to_datetime(df[date_column])
df['date'] = df[date_column].dt.date
daily_sentiment = df.groupby('date')[sentiment_column].agg(agg_method).reset_index()
daily_sentiment['date'] = pd.to_datetime(daily_sentiment['date'])
return daily_sentiment
def calculate_indicators(self, daily_sentiment, windows=None):
"""计算所有情绪指标"""
if windows is None:
windows = {'momentum': 5, 'volatility': 10, 'zscore': 20, 'percentile': 60, 'rsi': 14}
sentiment_df = daily_sentiment.sort_values('date').copy()
sentiment_col = sentiment_df.columns[1] # 假设第2列是情绪分数
# 动量指标
w = windows['momentum']
sentiment_df[f'{sentiment_col}_sma{w}'] = sentiment_df[sentiment_col].rolling(window=w).mean()
sentiment_df[f'{sentiment_col}_momentum'] = sentiment_df[sentiment_col] - sentiment_df[f'{sentiment_col}_sma{w}']
sentiment_df[f'{sentiment_col}_pct_change'] = sentiment_df[sentiment_col].pct_change() * 100
# 波动性指标
w = windows['volatility']
sentiment_df[f'{sentiment_col}_volatility'] = sentiment_df[sentiment_col].rolling(window=w).std()
# Z分数
w = windows['zscore']
rolling_mean = sentiment_df[sentiment_col].rolling(window=w).mean()
rolling_std = sentiment_df[sentiment_col].rolling(window=w).std()
sentiment_df[f'{sentiment_col}_zscore'] = (sentiment_df[sentiment_col] - rolling_mean) / rolling_std
# 百分位数
w = windows['percentile']
def rolling_percentile(x):
current = x.iloc[-1]
if len(x) < w // 2:
return np.nan
return stats.percentileofscore(x, current) / 100
sentiment_df[f'{sentiment_col}_percentile'] = sentiment_df[sentiment_col].rolling(window=w).apply(
rolling_percentile, raw=False)
# RSI指标
w = windows['rsi']
delta = sentiment_df[sentiment_col].diff()
gain = delta.clip(lower=0)
loss = -delta.clip(upper=0)
avg_gain = gain.rolling(window=w).mean()
avg_loss = loss.rolling(window=w).mean()
rs = avg_gain / avg_loss
sentiment_df[f'{sentiment_col}_rsi'] = 100 - (100 / (1 + rs))
return sentiment_df
def sentiment_divergence(self, sentiment_df, price_df, sentiment_col, price_col='close', window=10):
"""计算情绪与价格的背离"""
sentiment = sentiment_df.set_index('date')
price = price_df.set_index('date')
merged = pd.merge(sentiment, price, left_index=True, right_index=True, how='inner')
# 计算趋势方向
merged[f'{sentiment_col}_trend'] = np.sign(merged[sentiment_col].rolling(window=window).mean().diff())
merged[f'{price_col}_trend'] = np.sign(merged[price_col].rolling(window=window).mean().diff())
# 计算背离
merged['divergence'] = merged[f'{sentiment_col}_trend'] != merged[f'{price_col}_trend']
merged['bearish_divergence'] = ((merged[f'{price_col}_trend'] > 0) & (merged[f'{sentiment_col}_trend'] < 0))
merged['bullish_divergence'] = ((merged[f'{price_col}_trend'] < 0) & (merged[f'{sentiment_col}_trend'] > 0))
return merged.reset_index()
def plot_sentiment_indicators(self, df, date_column='date', price_column=None, figsize=(14, 10)):
"""绘制情绪指标图表"""
df[date_column] = pd.to_datetime(df[date_column])
sentiment_columns = [col for col in df.columns
if col != date_column and col != price_column and 'date' not in col.lower()]
fig, axes = plt.subplots(len(sentiment_columns), 1, figsize=figsize, sharex=True)
if len(sentiment_columns) == 1:
axes = [axes]
for i, col in enumerate(sentiment_columns):
ax = axes[i]
ax.plot(df[date_column], df[col], label=col)
ax.set_title(f'{col} Over Time')
ax.set_ylabel(col)
ax.grid(True, alpha=0.3)
ax.legend()
if price_column and price_column in df.columns:
ax2 = axes[-1].twinx()
ax2.plot(df[date_column], df[price_column], 'r-', alpha=0.5, label=price_column)
ax2.set_ylabel(price_column, color='r')
ax2.tick_params(axis='y', labelcolor='r')
ax2.legend(loc='upper right')
plt.tight_layout()
plt.xlabel('Date')
return fig
def analyze_relationship(self, sentiment_df, price_df, sentiment_col, price_col='close', lag_days=5, max_lag=10):
"""分析情绪与价格的关系"""
# 相关性分析
sentiment = sentiment_df.set_index('date')
price = price_df.set_index('date')
price[f'{price_col}_pct_change'] = price[price_col].pct_change()
merged = pd.merge(sentiment, price, left_index=True, right_index=True, how='inner')
# 相关性
correlations = []
for lag in range(1, lag_days + 1):
lagged_price_change = merged[f'{price_col}_pct_change'].shift(-lag)
valid_data = merged[[sentiment_col]].join(lagged_price_change).dropna()
if len(valid_data) > 5: # 确保有足够数据
corr, p_value = stats.pearsonr(valid_data[sentiment_col], valid_data[f'{price_col}_pct_change'])
correlations.append({
'lag_days': lag,
'correlation': corr,
'p_value': p_value,
'significant': p_value < 0.05
})
corr_result = pd.DataFrame(correlations) if correlations else None
# 格兰杰因果关系检验
sentiment_series = merged[sentiment_col].dropna()
price_change_series = merged[f'{price_col}_pct_change'].dropna()
min_length = min(len(sentiment_series), len(price_change_series))
if min_length > max_lag + 2:
data = pd.DataFrame({
'sentiment': sentiment_series.iloc[-min_length:].values,
'price_change': price_change_series.iloc[-min_length:].values
})
gc_results = []
for lag in range(1, max_lag + 1):
gc_res = sm.tsa.stattools.grangercausalitytests(data[['sentiment', 'price_change']], lag, verbose=False)
p_value = gc_res[lag][0]['ssr_chi2test'][1]
gc_results.append({
'lag': lag,
'p_value': p_value,
'significant': p_value < 0.05
})
gc_result = pd.DataFrame(gc_results)
else:
gc_result = None
return {'correlation': corr_result, 'granger_causality': gc_result}
# 使用示例
if __name__ == "__main__":
# 创建示例数据
sentiment_data = pd.DataFrame({
'date': pd.date_range(start='2023-01-01', periods=60),
'sentiment_score': np.random.normal(0.2, 0.5, 60)
})
price_data = pd.DataFrame({
'date': pd.date_range(start='2023-01-01', periods=60),
'close': np.cumsum(np.random.normal(0, 1, 60)) + 100
})
# 初始化并计算指标
indicator = SentimentIndicator()
daily_sentiment = indicator.calculate_daily_sentiment(sentiment_data, 'date', 'sentiment_score')
full_indicators = indicator.calculate_indicators(daily_sentiment)
# 绘制情绪指标
fig = indicator.plot_sentiment_indicators(full_indicators, price_column='close')
plt.show()
# 分析情绪与价格的关系
analysis = indicator.analyze_relationship(full_indicators, price_data, 'sentiment_score_zscore')
print("情绪与未来价格的相关性分析:")
print(analysis['correlation'])
# 情绪与价格的背离分析
divergence = indicator.sentiment_divergence(full_indicators, price_data, 'sentiment_score')
print("\n情绪与价格的背离:")
print(divergence[['date', 'divergence', 'bearish_divergence', 'bullish_divergence']].tail())import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import pytz
import backtrader as bt
import warnings
import logging
# 设置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger('SentimentStrategy')
# 忽略警告
warnings.filterwarnings('ignore')
class SentimentData(bt.feeds.PandasData):
"""
自定义数据源，增加情绪指标列
"""
lines = ('sentiment', 'sentiment_z', 'sentiment_momentum', 'sentiment_volatility', 'sentiment_rsi')
params = (
('sentiment', -1),
('sentiment_z', -1),
('sentiment_momentum', -1),
('sentiment_volatility', -1),
('sentiment_rsi', -1),
)
class BaseSentimentStrategy(bt.Strategy):
"""
基础情绪分析策略类
"""
params = (
('sentiment_threshold_high', 1.0), # 高情绪阈值
('sentiment_threshold_low', -1.0), # 低情绪阈值
('position_size', 1.0), # 仓位大小占总资金的比例
('stop_loss', 0.03), # 止损比例
('take_profit', 0.05), # 止盈比例
('use_zscore', True), # 是否使用Z分数
('use_momentum', False), # 是否使用动量
('use_volatility', False), # 是否使用波动性
('use_rsi', False), # 是否使用RSI
('rsi_high', 70), # RSI高阈值
('rsi_low', 30), # RSI低阈值
('trail_percent', 0.0), # 追踪止损比例（0表示不使用）
)
def log(self, txt, dt=None):
"""记录策略日志"""
dt = dt or self.datas[0].datetime.date(0)
logger.info(f'{dt.isoformat()} - {txt}')
def __init__(self):
"""初始化策略"""
# 存储数据线引用
self.dataclose = self.datas[0].close
self.sentiment = self.datas[0].sentiment
self.sentiment_z = self.datas[0].sentiment_z
self.sentiment_momentum = self.datas[0].sentiment_momentum
self.sentiment_volatility = self.datas[0].sentiment_volatility
self.sentiment_rsi = self.datas[0].sentiment_rsi
# 订单和仓位跟踪
self.order = None
self.buy_price = None
self.buy_comm = None
# 用于存储止损和止盈价格
self.stop_price = None
self.target_price = None
self.trail_price = None
# 绘图指标
if self.p.use_zscore:
bt.indicators.ExponentialMovingAverage(self.sentiment_z, period=10)
if self.p.use_momentum:
bt.indicators.ExponentialMovingAverage(self.sentiment_momentum, period=10)
if self.p.use_volatility:
bt.indicators.ExponentialMovingAverage(self.sentiment_volatility, period=10)
if self.p.use_rsi:
bt.indicators.ExponentialMovingAverage(self.sentiment_rsi, period=10)
def notify_order(self, order):
"""处理订单状态通知"""
if order.status in [order.Submitted, order.Accepted]:
# 订单已提交/接受 - 无操作
return
# 检查订单是否已完成
if order.status in [order.Completed]:
if order.isbuy():
self.log(f'买入执行: 价格={order.executed.price:.2f}, 成本={order.executed.value:.2f}, 手续费={order.executed.comm:.2f}')
self.buy_price = order.executed.price
self.buy_comm = order.executed.comm
# 设置止损和止盈价格
self.stop_price = self.buy_price * (1.0 - self.p.stop_loss)
self.target_price = self.buy_price * (1.0 + self.p.take_profit)
# 如果使用追踪止损，设置初始追踪价格
if self.p.trail_percent > 0:
self.trail_price = self.buy_price * (1.0 - self.p.trail_percent)
elif order.issell():
self.log(f'卖出执行: 价格={order.executed.price:.2f}, 成本={order.executed.value:.2f}, 手续费={order.executed.comm:.2f}')
# 重置止损和止盈价格
self.stop_price = None
self.target_price = None
self.trail_price = None
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('订单取消/保证金不足/拒绝')
# 重置订单
self.order = None
def notify_trade(self, trade):
"""处理交易结果通知"""
if not trade.isclosed:
return
self.log(f'交易利润: 毛利={trade.pnl:.2f}, 净利={trade.pnlcomm:.2f}')
def get_sentiment_signal(self):
"""
获取情绪信号
返回: 1 (看涨), -1 (看跌), 0 (中性)
"""
signal = 0
# 根据不同情绪指标生成信号
if self.p.use_zscore:
if self.sentiment_z[0] > self.p.sentiment_threshold_high:
signal += 1
elif self.sentiment_z[0] < self.p.sentiment_threshold_low:
signal -= 1
if self.p.use_momentum:
if self.sentiment_momentum[0] > 0:
signal += 1
elif self.sentiment_momentum[0] < 0:
signal -= 1
if self.p.use_volatility:
# 波动性高时减少信号强度
if self.sentiment_volatility[0] > np.nanmean(self.sentiment_volatility.get(size=30)):
signal = signal * 0.5
if self.p.use_rsi:
if self.sentiment_rsi[0] > self.p.rsi_high:
signal += 1
elif self.sentiment_rsi[0] < self.p.rsi_low:
signal -= 1
# 标准化信号
if signal > 0:
return 1
elif signal < 0:
return -1
else:
return 0
def next(self):
"""
策略核心逻辑：每个交易日执行一次
"""
# 跳过缺失数据
if np.isnan(self.sentiment[0]):
return
# 如果有未完成的订单，不执行新操作
if self.order:
return
# 获取情绪信号
sentiment_signal = self.get_sentiment_signal()
# 更新追踪止损价格（如果启用）
if self.position and self.p.trail_percent > 0:
new_trail_price = self.dataclose[0] * (1.0 - self.p.trail_percent)
if new_trail_price > self.trail_price:
self.trail_price = new_trail_price
self.log(f'更新追踪止损价格: {self.trail_price:.2f}')
# 检查止损和止盈条件
if self.position:
# 止损
if self.dataclose[0] < self.stop_price:
self.log(f'触发止损: 当前价格={self.dataclose[0]:.2f}, 止损价格={self.stop_price:.2f}')
self.order = self.sell()
return
# 追踪止损
if self.p.trail_percent > 0 and self.dataclose[0] < self.trail_price:
self.log(f'触发追踪止损: 当前价格={self.dataclose[0]:.2f}, 追踪价格={self.trail_price:.2f}')
self.order = self.sell()
return
# 止盈
if self.dataclose[0] > self.target_price:
self.log(f'触发止盈: 当前价格={self.dataclose[0]:.2f}, 目标价格={self.target_price:.2f}')
self.order = self.sell()
return
# 基于情绪信号执行交易
if not self.position: # 当前无仓位
if sentiment_signal > 0: # 情绪积极，买入信号
cash_to_use = self.broker.getcash() * self.p.position_size
size = cash_to_use / self.dataclose[0]
self.log(f'买入信号: 价格={self.dataclose[0]:.2f}, 规模={size:.2f}, 情绪分数={self.sentiment_z[0]:.2f}')
self.order = self.buy(size=size)
else: # 当前有仓位
if sentiment_signal < 0: # 情绪消极，卖出信号
self.log(f'卖出信号: 价格={self.dataclose[0]:.2f}, 情绪分数={self.sentiment_z[0]:.2f}')
self.order = self.sell()class SentimentBacktester:
"""情绪分析策略回测器"""
def __init__(self, price_data, sentiment_data, initial_cash=100000.0):
"""
初始化回测器
参数:
price_data (pd.DataFrame): 包含OHLCV数据的DataFrame
sentiment_data (pd.DataFrame): 包含情绪指标的DataFrame
initial_cash (float): 初始资金
"""
self.price_data = price_data
self.sentiment_data = sentiment_data
self.initial_cash = initial_cash
self.cerebro = bt.Cerebro()
self.cerebro.broker.setcash(initial_cash)
self.cerebro.broker.setcommission(commission=0.001) # 0.1%手续费
# 设置分析器
self.cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')
self.cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')
self.cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
self.cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='trades')
def prepare_data(self):
"""准备回测数据"""
# 确保两个数据框的日期格式一致
self.price_data['date'] = pd.to_datetime(self.price_data['date'])
self.sentiment_data['date'] = pd.to_datetime(self.sentiment_data['date'])
# 合并价格和情绪数据
merged_data = pd.merge(
self.price_data,
self.sentiment_data,
on='date',
how='left'
)
# 确保日期是索引
merged_data.set_index('date', inplace=True)
# 创建回测数据源
data = SentimentData(
dataname=merged_data,
open=merged_data['open'],
high=merged_data['high'],
low=merged_data['low'],
close=merged_data['close'],
volume=merged_data['volume'],
sentiment=merged_data['sentiment_score'],
sentiment_z=merged_data['sentiment_score_zscore'],
sentiment_momentum=merged_data['sentiment_score_momentum'],
sentiment_volatility=merged_data['sentiment_score_volatility'],
sentiment_rsi=merged_data['sentiment_score_rsi'],
openinterest=None,
datetime=None, # 使用索引作为日期
)
self.cerebro.adddata(data)
return data
def run_backtest(self, strategy_class=BaseSentimentStrategy, **strategy_params):
"""
运行回测
参数:
strategy_class: 要使用的策略类
**strategy_params: 策略参数
返回:
tuple: (最终价值, 分析结果)
"""
# 添加策略
self.cerebro.addstrategy(strategy_class, **strategy_params)
# 运行回测
results = self.cerebro.run()
self.strat = results[0]
# 获取回测结果
final_value = self.cerebro.broker.getvalue()
profit_perc = (final_value - self.initial_cash) / self.initial_cash * 100
# 收集分析结果
analysis = {
'initial_cash': self.initial_cash,
'final_value': final_value,
'profit_loss': final_value - self.initial_cash,
'profit_perc': profit_perc,
'sharpe_ratio': self.strat.analyzers.sharpe.get_analysis()['sharperatio'],
'max_drawdown': self.strat.analyzers.drawdown.get_analysis()['max']['drawdown'],
'return_perc': self.strat.analyzers.returns.get_analysis()['rtot'] * 100,
}
# 分析交易情况
trade_analysis = self.strat.analyzers.trades.get_analysis()
# 总交易数
analysis['total_trades'] = trade_analysis.get('total', 0)
# 盈利交易
if 'won' in trade_analysis:
analysis['winning_trades'] = trade_analysis['won']['total']
analysis['win_rate'] = (trade_analysis['won']['total'] / analysis['total_trades']) * 100 if analysis['total_trades'] > 0 else 0
analysis['avg_winning_trade'] = trade_analysis['won']['pnl']['average']
else:
analysis['winning_trades'] = 0
analysis['win_rate'] = 0
analysis['avg_winning_trade'] = 0
# 亏损交易
if 'lost' in trade_analysis:
analysis['losing_trades'] = trade_analysis['lost']['total']
analysis['loss_rate'] = (trade_analysis['lost']['total'] / analysis['total_trades']) * 100 if analysis['total_trades'] > 0 else 0
analysis['avg_losing_trade'] = trade_analysis['lost']['pnl']['average']
else:
analysis['losing_trades'] = 0
analysis['loss_rate'] = 0
analysis['avg_losing_trade'] = 0
# 利润因子
if analysis['losing_trades'] > 0 and 'lost' in trade_analysis and 'won' in trade_analysis:
analysis['profit_factor'] = abs(trade_analysis['won']['pnl']['total'] / trade_analysis['lost']['pnl']['total']) if trade_analysis['lost']['pnl']['total'] != 0 else float('inf')
else:
analysis['profit_factor'] = float('inf') if analysis['winning_trades'] > 0 else 0
return final_value, analysis
def plot_results(self, filename=None):
"""
绘制回测结果
参数:
filename (str, optional): 保存图表的文件名
"""
plt.figure(figsize=(15, 10))
plt.rcParams['axes.grid'] = True
# 绘制回测图表
self.cerebro.plot(style='candlestick', barup='green', bardown='red',
volup='green', voldown='red',
plotdist=0.1,
start=0, end=len(self.price_data))
if filename:
plt.savefig(filename)
plt.show()
def optimize_strategy(self, strategy_class=BaseSentimentStrategy, param_grid=None):
"""
优化策略参数
参数:
strategy_class: 要优化的策略类
param_grid (dict): 参数网格，格式为 {参数名: [参数值列表]}
返回:
tuple: (最佳参数, 最佳性能)
"""
if param_grid is None:
param_grid = {
'sentiment_threshold_high': [0.5, 1.0, 1.5, 2.0],
'sentiment_threshold_low': [-0.5, -1.0, -1.5, -2.0],
'position_size': [0.1, 0.25, 0.5, 1.0],
'stop_loss': [0.02, 0.03, 0.05],
'take_profit': [0.03, 0.05, 0.08],
'use_zscore': [True],
'use_momentum': [False, True],
'use_rsi': [False, True]
}
# 创建参数组合
param_combinations = self._create_param_combinations(param_grid)
best_sharpe = -np.inf
best_params = None
best_performance = None
# 遍历所有参数组合
for params in param_combinations:
# 重置cerebro实例
self.cerebro = bt.Cerebro()
self.cerebro.broker.setcash(self.initial_cash)
self.cerebro.broker.setcommission(commission=0.001)
# 设置分析器
self.cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')
self.cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')
self.cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
self.cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='trades')
# 准备数据
self.prepare_data()
# 添加策略
self.cerebro.addstrategy(strategy_class, **params)
# 运行回测
results = self.cerebro.run()
strat = results[0]
# 获取夏普比率
sharpe = strat.analyzers.sharpe.get_analysis()['sharperatio']
# 如果找到更好的参数
if sharpe > best_sharpe and not np.isnan(sharpe):
best_sharpe = sharpe
best_params = params
# 收集最佳性能指标
best_performance = {
'final_value': self.cerebro.broker.getvalue(),
'profit_perc': (self.cerebro.broker.getvalue() - self.initial_cash) / self.initial_cash * 100,
'sharpe_ratio': sharpe,
'max_drawdown': strat.analyzers.drawdown.get_analysis()['max']['drawdown'],
'return_perc': strat.analyzers.returns.get_analysis()['rtot'] * 100,
}
# 分析交易
trade_analysis = strat.analyzers.trades.get_analysis()
best_performance['total_trades'] = trade_analysis.get('total', 0)
if 'won' in trade_analysis:
best_performance['winning_trades'] = trade_analysis['won']['total']
best_performance['win_rate'] = (trade_analysis['won']['total'] / best_performance['total_trades']) * 100 if best_performance['total_trades'] > 0 else 0
else:
best_performance['winning_trades'] = 0
best_performance['win_rate'] = 0
if 'lost' in trade_analysis:
best_performance['losing_trades'] = trade_analysis['lost']['total']
best_performance['loss_rate'] = (trade_analysis['lost']['total'] / best_performance['total_trades']) * 100 if best_performance['total_trades'] > 0 else 0
else:
best_performance['losing_trades'] = 0
best_performance['loss_rate'] = 0
return best_params, best_performance
def _create_param_combinations(self, param_grid):
"""创建参数网格的所有组合"""
import itertools
keys = param_grid.keys()
values = param_grid.values()
combinations = list(itertools.product(*values))
param_combinations = []
for combo in combinations:
param_dict = dict(zip(keys, combo))
param_combinations.append(param_dict)
return param_combinations
# 使用示例
if __name__ == "__main__":
# 创建示例价格数据
dates = pd.date_range(start='2023-01-01', periods=100)
price_data = pd.DataFrame({
'date': dates,
'open': np.random.normal(100, 2, 100),
'high': np.random.normal(102, 2, 100),
'low': np.random.normal(98, 2, 100),
'close': np.random.normal(100, 2, 100),
'volume': np.random.randint(1000000, 5000000, 100)
})
# 生成一些具有趋势的价格
for i in range(1, len(price_data)):
price_data.loc[i, 'close'] = price_data.loc[i-1, 'close'] * (1 + np.random.normal(0.001, 0.02))
price_data.loc[i, 'high'] = max(price_data.loc[i, 'close'] * (1 + abs(np.random.normal(0, 0.01))),
price_data.loc[i, 'open'])
price_data.loc[i, 'low'] = min(price_data.loc[i, 'close'] * (1 - abs(np.random.normal(0, 0.01))),
price_data.loc[i, 'open'])
# 创建示例情绪数据
sentiment_data = pd.DataFrame({
'date': dates,
'sentiment_score': np.random.normal(0.2, 0.5, 100),
'sentiment_score_zscore': np.random.normal(0, 1, 100),
'sentiment_score_momentum': np.random.normal(0, 0.2, 100),
'sentiment_score_volatility': np.random.normal(0.2, 0.1, 100),
'sentiment_score_rsi': np.random.uniform(30, 70, 100)
})
# 使情绪数据呈现正相关性
for i in range(1, len(sentiment_data)):
price_change = price_data.loc[i, 'close'] / price_data.loc[i-1, 'close'] - 1
sentiment_data.loc[i, 'sentiment_score'] = sentiment_data.loc[i-1, 'sentiment_score'] + price_change * 2 + np.random.normal(0, 0.2)
sentiment_data.loc[i, 'sentiment_score_zscore'] = (sentiment_data.loc[i, 'sentiment_score'] - sentiment_data['sentiment_score'].mean()) / sentiment_data['sentiment_score'].std()
# 初始化回测器
backtester = SentimentBacktester(price_data, sentiment_data)
# 准备数据
backtester.prepare_data()
# 运行回测
final_value, analysis = backtester.run_backtest(
sentiment_threshold_high=1.0,
sentiment_threshold_low=-1.0,
position_size=0.5,
stop_loss=0.03,
take_profit=0.05,
use_zscore=True,
use_momentum=True
)
# 打印回测结果
print("回测结果:")
for key, value in analysis.items():
print(f"{key}: {value}")
# 绘制回测结果
backtester.plot_results()
# 优化策略参数
best_params, best_performance = backtester.optimize_strategy(
param_grid={
'sentiment_threshold_high': [0.5, 1.0, 1.5],
'sentiment_threshold_low': [-0.5, -1.0, -1.5],
'position_size': [0.25, 0.5],
'use_zscore': [True],
'use_momentum': [False, True]
}
)
print("\n最佳参数:")
for key, value in best_params.items():
print(f"{key}: {value}")
print("\n最佳性能:")
for key, value in best_performance.items():
print(f"{key}: {value}")
# 使用示例
if __name__ == "__main__":
# 创建示例价格数据
dates = pd.date_range(start='2023-01-01', periods=100)
price_data = pd.DataFrame({
'date': dates,
'open': np.random.normal(100, 2, 100),
'high': np.random.normal(102, 2, 100),
'low': np.random.normal(98, 2, 100),
'close': np.random.normal(100, 2, 100),
'volume': np.random.randint(1000000, 5000000, 100)
})
# 生成一些具有趋势的价格
for i in range(1, len(price_data)):
price_data.loc[i, 'close'] = price_data.loc[i-1, 'close'] * (1 + np.random.normal(0.001, 0.02))
price_data.loc[i, 'high'] = max(price_data.loc[i, 'close'] * (1 + abs(np.random.normal(0, 0.01))),
price_data.loc[i, 'open'])
price_data.loc[i, 'low'] = min(price_data.loc[i, 'close'] * (1 - abs(np.random.normal(0, 0.01))),
price_data.loc[i, 'open'])
# 创建示例情绪数据
sentiment_data = pd.DataFrame({
'date': dates,
'sentiment_score': np.random.normal(0.2, 0.5, 100),
'sentiment_score_zscore': np.random.normal(0, 1, 100),
'sentiment_score_momentum': np.random.normal(0, 0.2, 100),
'sentiment_score_volatility': np.random.normal(0.2, 0.1, 100),
'sentiment_score_rsi': np.random.uniform(30, 70, 100)
})
# 使情绪数据呈现正相关性
for i in range(1, len(sentiment_data)):
price_change = price_data.loc[i, 'close'] / price_data.loc[i-1, 'close'] - 1
sentiment_data.loc[i, 'sentiment_score'] = sentiment_data.loc[i-1, 'sentiment_score'] + price_change * 2 + np.random.normal(0, 0.2)
sentiment_data.loc[i, 'sentiment_score_zscore'] = (sentiment_data.loc[i, 'sentiment_score'] - sentiment_data['sentiment_score'].mean()) / sentiment_data['sentiment_score'].std()
# 初始化回测器
backtester = SentimentBacktester(price_data, sentiment_data)
# 准备数据
backtester.prepare_data()
# 运行回测
final_value, analysis = backtester.run_backtest(
sentiment_threshold_high=1.0,
sentiment_threshold_low=-1.0,
position_size=0.5,
stop_loss=0.03,
take_profit=0.05,
use_zscore=True,
use_momentum=True
)
# 打印回测结果
print("回测结果:")
for key, value in analysis.items():
print(f"{key}: {value}")
# 绘制回测结果
backtester.plot_results()
# 优化策略参数
best_params, best_performance = backtester.optimize_strategy(
param_grid={
'sentiment_threshold_high': [0.5, 1.0, 1.5],
'sentiment_threshold_low': [-0.5, -1.0, -1.5],
'position_size': [0.25, 0.5],
'use_zscore': [True],
'use_momentum': [False, True]
}
)
print("\n最佳参数:")
for key, value in best_params.items():
print(f"{key}: {value}")
print("\n最佳性能:")
for key, value in best_performance.items():
print(f"{key}: {value}") 在这部分，你需要利用提供的基础NLP处理框架，为特定市场（如股票、加密货币）收集和处理文本数据。
关键任务：
import pandas as pd
import numpy as np
import re
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import spacy
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
# 可能需要添加其他库，如requests用于API调用，BeautifulSoup用于网页解析等
# 下载必要的NLTK资源
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
class FinancialTextProcessor:
def __init__(self, market_type='stock'):
"""
初始化文本处理器
参数:
market_type (str): 市场类型，可选 'stock' 或 'crypto'
"""
self.market_type = market_type
self.stop_words = set(stopwords.words('english'))
self.lemmatizer = WordNetLemmatizer()
# 加载spaCy模型用于命名实体识别
self.nlp = spacy.load('en_core_web_sm')
# 初始化特定市场的术语词典
self.market_terms = self._init_market_terms()
def _init_market_terms(self):
"""
初始化特定市场的术语词典
返回:
dict: 包含市场特定术语的字典
"""
if self.market_type == 'stock':
# TODO: 实现股票市场术语词典
market_terms = {
'bullish': 1, # 正面情感
'bearish': -1, # 负面情感
# 添加更多股票市场术语...
}
elif self.market_type == 'crypto':
# TODO: 实现加密货币市场术语词典
market_terms = {
'moon': 1, # 正面情感
'dump': -1, # 负面情感
# 添加更多加密货币市场术语...
}
else:
market_terms = {}
return market_terms
def collect_data(self, source, params=None):
"""
从指定来源收集文本数据
参数:
source (str): 数据来源，如 'news_api', 'twitter', 'reddit'
params (dict): API调用参数
返回:
DataFrame: 包含收集到的文本数据
"""
# TODO: 实现数据收集功能
# 这里可以使用不同的API或网页抓取技术获取数据
if source == 'news_api':
# 实现新闻API数据收集
pass
elif source == 'twitter':
# 实现Twitter数据收集
pass
elif source == 'reddit':
# 实现Reddit数据收集
pass
# 返回收集到的数据，例如:
# return pd.DataFrame({'text': [...], 'date': [...], 'source': [...]})
return pd.DataFrame()
def clean_text(self, text):
"""
清洗文本数据
参数:
text (str): 输入文本
返回:
str: 清洗后的文本
"""
# TODO: 实现文本清洗功能
# 1. 转换为小写
text = text.lower()
# 2. 移除URL
# TODO: 添加正则表达式以移除URL
# 3. 移除特殊字符和数字
# TODO: 添加正则表达式以移除特殊字符和数字
# 4. 移除额外的空格
text = re.sub(r'\s+', ' ', text).strip()
return text
def preprocess(self, df, text_column='text'):
"""
预处理DataFrame中的文本数据
参数:
df (DataFrame): 包含文本数据的DataFrame
text_column (str): 文本列名
返回:
DataFrame: 包含预处理后文本的DataFrame
"""
# 清洗文本
df['cleaned_text'] = df[text_column].apply(self.clean_text)
# 标记化并去除停用词
df['tokens'] = df['cleaned_text'].apply(self._tokenize_and_clean)
# 词形还原
df['lemmatized'] = df['tokens'].apply(self._lemmatize)
return df
def _tokenize_and_clean(self, text):
"""
标记化文本并移除停用词
参数:
text (str): 输入文本
返回:
list: 标记化且去除停用词的词列表
"""
# TODO: 实现标记化和停用词去除
tokens = word_tokenize(text)
tokens = [token for token in tokens if token not in self.stop_words]
return tokens
def _lemmatize(self, tokens):
"""
对词列表进行词形还原
参数:
tokens (list): 词列表
返回:
list: 词形还原后的词列表
"""
# TODO: 实现词形还原
return [self.lemmatizer.lemmatize(token) for token in tokens]
def extract_entities(self, text):
"""
从文本中提取命名实体
参数:
text (str): 输入文本
返回:
dict: 按类型分组的命名实体
"""
# TODO: 实现命名实体识别
doc = self.nlp(text)
entities = {}
for ent in doc.ents:
if ent.label_ not in entities:
entities[ent.label_] = []
entities[ent.label_].append(ent.text)
return entities
def extract_keywords(self, text, n=10):
"""
从文本中提取关键词
参数:
text (str): 输入文本
n (int): 返回的关键词数量
返回:
list: 关键词列表
"""
# TODO: 实现关键词提取
# 可以使用TF-IDF或其他关键词提取技术
# 示例：使用简单的词频
tokens = self._tokenize_and_clean(text)
freq_dist = nltk.FreqDist(tokens)
keywords = [word for word, freq in freq_dist.most_common(n)]
return keywords
def extract_market_sentiment(self, text):
"""
从文本中提取市场情感
参数:
text (str): 输入文本
返回:
float: 情感得分
"""
# TODO: 实现市场特定情感分析
tokens = self._tokenize_and_clean(text)
sentiment_score = 0
for token in tokens:
if token in self.market_terms:
sentiment_score += self.market_terms[token]
return sentiment_score
def create_feature_vector(self, df, method='tfidf'):
"""
为文本数据创建特征向量
参数:
df (DataFrame): 包含预处理文本的DataFrame
method (str): 特征提取方法，可选 'tfidf', 'count', 'custom'
返回:
tuple: (特征矩阵, 特征名称)
"""
# TODO: 实现特征向量创建
if method == 'tfidf':
# 使用TF-IDF
vectorizer = TfidfVectorizer(max_features=1000)
X = vectorizer.fit_transform(df['cleaned_text'])
feature_names = vectorizer.get_feature_names_out()
elif method == 'count':
# 使用词袋模型
vectorizer = CountVectorizer(max_features=1000)
X = vectorizer.fit_transform(df['cleaned_text'])
feature_names = vectorizer.get_feature_names_out()
elif method == 'custom':
# TODO: 实现自定义特征提取
# 例如，结合TF-IDF、情感得分、实体数量等
pass
return X, feature_names
def enrich_features_with_domain_knowledge(self, X, feature_names):
"""
使用领域知识丰富特征
参数:
X (sparse matrix): 特征矩阵
feature_names (list): 特征名称
返回:
tuple: (增强的特征矩阵, 更新的特征名称)
"""
# TODO: 实现基于领域知识的特征增强
# 例如，添加市场术语频率、情感强度等特征
return X, feature_names
# 使用示例
if __name__ == "__main__":
# 初始化处理器
processor = FinancialTextProcessor(market_type='stock')
# 收集数据示例
# data = processor.collect_data(source='news_api', params={'q': 'Tesla', 'from': '2023-01-01'})
# 使用示例数据
data = pd.DataFrame({
'text': [
"Tesla stock surges after strong quarterly earnings report.",
"Investors are bearish on tech stocks due to rising interest rates.",
"Bitcoin plummets 15% as regulatory concerns grow."
]
})
# 预处理文本
processed_data = processor.preprocess(data)
print("预处理示例:")
print(processed_data[['cleaned_text', 'tokens']].head())
# 提取实体
print("\n实体识别示例:")
entities = processor.extract_entities(data['text'][0])
print(entities)
# 提取关键词
print("\n关键词提取示例:")
keywords = processor.extract_keywords(data['text'][0])
print(keywords)
# 提取市场情感
print("\n市场情感示例:")
sentiment = processor.extract_market_sentiment(data['text'][1])
print(sentiment)
# 创建特征向量
print("\n特征提取示例:")
X, feature_names = processor.create_feature_vector(processed_data)
print(f"特征数量: {len(feature_names)}")
print(f"特征矩阵形状: {X.shape}")基于处理后的文本数据，你需要开发和评估适用于金融市场的情绪分析模型。
关键任务：
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
# 可能需要导入更多库，如NLTK、spaCy、transformers等
class FinancialSentimentAnalyzer:
"""
金融市场情绪分析框架类，包含不同的情感分析技术实现和评估方法
"""
def __init__(self, data_path=None):
"""
初始化分析器
参数:
data_path: 数据文件路径
"""
self.data = None
self.lexicon = None
self.ml_model = None
self.dl_model = None
if data_path:
self.load_data(data_path)
def load_data(self, data_path):
"""
加载金融文本数据
参数:
data_path: 数据文件路径
"""
# TODO: 实现数据加载逻辑
self.data = pd.read_csv(data_path) # 或其他格式
print(f"数据加载完成，共 {len(self.data)} 条记录")
def preprocess_text(self, text_column):
"""
预处理文本数据
参数:
text_column: 文本列名
"""
# TODO: 实现文本预处理逻辑
# 如：分词、去除停用词、词干提取、处理特殊符号等
pass
def handle_negations(self, texts):
"""
处理金融文本中的否定词
参数:
texts: 文本列表
返回:
处理后的文本
"""
# TODO: 实现否定词处理逻辑
# 如: "not good" -> "bad", "no increase" -> "decrease"
pass
# =========================== 词典法 ===========================
def load_lexicon(self, lexicon_path):
"""
加载情感词典
参数:
lexicon_path: 情感词典路径
"""
# TODO: 实现情感词典加载逻辑
pass
def lexicon_based_analysis(self, texts):
"""
基于词典的情感分析
参数:
texts: 要分析的文本列表
返回:
情感分数列表
"""
# TODO: 实现基于词典的情感分析
# 如：计算文本中积极/消极词的数量、权重等
pass
# =========================== 机器学习法 ===========================
def extract_ml_features(self, texts):
"""
提取用于机器学习的特征
参数:
texts: 文本列表
返回:
特征矩阵
"""
# TODO: 实现特征提取逻辑
# 如：TF-IDF、词袋模型、n-gram特征等
pass
def train_ml_model(self, features, labels):
"""
训练机器学习模型
参数:
features: 特征矩阵
labels: 标签列表
"""
# TODO: 实现机器学习模型训练
# 如：SVM、随机森林、逻辑回归等
pass
def predict_ml(self, features):
"""
使用机器学习模型预测情感
参数:
features: 特征矩阵
返回:
预测结果
"""
# TODO: 实现机器学习预测逻辑
pass
# =========================== 深度学习法 ===========================
def prepare_dl_data(self, texts, labels=None):
"""
准备深度学习模型的输入数据
参数:
texts: 文本列表
labels: 标签列表（可选）
返回:
模型输入数据
"""
# TODO: 实现数据准备逻辑
# 如：词嵌入、序列填充等
pass
def build_dl_model(self):
"""
构建深度学习模型
返回:
构建的模型
"""
# TODO: 实现深度学习模型构建
# 如：LSTM、Transformer、BERT等
pass
def train_dl_model(self, train_data, train_labels, val_data=None, val_labels=None):
"""
训练深度学习模型
参数:
train_data: 训练数据
train_labels: 训练标签
val_data: 验证数据（可选）
val_labels: 验证标签（可选）
"""
# TODO: 实现深度学习模型训练
pass
def predict_dl(self, data):
"""
使用深度学习模型预测情感
参数:
data: 输入数据
返回:
预测结果
"""
# TODO: 实现深度学习预测逻辑
pass
# =========================== 模型评估 ===========================
def evaluate_model(self, true_labels, predicted_labels, model_name):
"""
评估模型性能
参数:
true_labels: 真实标签
predicted_labels: 预测标签
model_name: 模型名称
返回:
评估结果
"""
print(f"===== {model_name} 模型评估 =====")
# 计算准确率
accuracy = accuracy_score(true_labels, predicted_labels)
print(f"准确率: {accuracy:.4f}")
# 混淆矩阵
cm = confusion_matrix(true_labels, predicted_labels)
print("混淆矩阵:")
print(cm)
# 分类报告
report = classification_report(true_labels, predicted_labels)
print("分类报告:")
print(report)
# TODO: 添加更多评估指标，如F1分数、ROC曲线等
return {
'accuracy': accuracy,
'confusion_matrix': cm,
'classification_report': report
}
def compare_models(self, models_results):
"""
比较不同模型的性能
参数:
models_results: 不同模型的评估结果
"""
# TODO: 实现模型比较逻辑
# 如：绘制性能比较图表等
pass
def correlate_with_market(self, sentiment_scores, market_data):
"""
分析情感分数与市场表现的相关性
参数:
sentiment_scores: 情感分数
market_data: 市场数据
返回:
相关性分析结果
"""
# TODO: 实现相关性分析逻辑
# 如：计算相关系数、滞后相关等
pass
# 示例使用流程
def main():
# 初始化分析器
analyzer = FinancialSentimentAnalyzer()
# 加载数据
analyzer.load_data('financial_news.csv') # 替换为实际数据文件
# 数据预处理
analyzer.preprocess_text('text_column')
# 划分训练集和测试集
X = analyzer.data['text_column'] # 替换为实际文本列
y = analyzer.data['sentiment'] # 替换为实际标签列
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# ============ 词典法 ============
analyzer.load_lexicon('financial_lexicon.txt') # 替换为实际词典文件
train_scores_lexicon = analyzer.lexicon_based_analysis(X_train)
test_scores_lexicon = analyzer.lexicon_based_analysis(X_test)
# 将连续的情感分数转换为离散类别（看涨、看跌、中性）
# TODO: 实现分数到类别的转换
y_pred_lexicon = [] # 转换后的预测类别
# 评估词典法
lexicon_results = analyzer.evaluate_model(y_test, y_pred_lexicon, "词典法")
# ============ 机器学习法 ============
# 特征提取
X_train_features = analyzer.extract_ml_features(X_train)
X_test_features = analyzer.extract_ml_features(X_test)
# 训练模型
analyzer.train_ml_model(X_train_features, y_train)
# 预测
y_pred_ml = analyzer.predict_ml(X_test_features)
# 评估机器学习模型
ml_results = analyzer.evaluate_model(y_test, y_pred_ml, "机器学习")
# ============ 深度学习法 ============
# 准备数据
train_data = analyzer.prepare_dl_data(X_train, y_train)
test_data = analyzer.prepare_dl_data(X_test)
# 构建并训练模型
analyzer.build_dl_model()
analyzer.train_dl_model(train_data[0], train_data[1])
# 预测
y_pred_dl = analyzer.predict_dl(test_data)
# 评估深度学习模型
dl_results = analyzer.evaluate_model(y_test, y_pred_dl, "深度学习")
# ============ 比较模型 ============
all_results = {
'词典法': lexicon_results,
'机器学习': ml_results,
'深度学习': dl_results
}
analyzer.compare_models(all_results)
# ============ 与市场数据相关性分析 ============
# 加载市场数据
market_data = pd.read_csv('market_data.csv') # 替换为实际市场数据文件
# 分析相关性
correlation_results = analyzer.correlate_with_market(y_pred_dl, market_data)
print("分析完成!")
if __name__ == "__main__":
main()利用情绪分析结果，你需要设计有效的情绪指标并分析其与市场表现的关联。
关键任务：
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from typing import Dict, List, Tuple, Optional, Union
from scipy import stats
import statsmodels.api as sm
from statsmodels.tsa.stattools import grangercausalitytest
from datetime import datetime, timedelta
class SentimentIndicator:
"""情绪指标计算和分析的基类"""
def __init__(self, sentiment_data: pd.DataFrame, price_data: pd.DataFrame):
"""
初始化情绪指标分析器
参数:
sentiment_data: 包含情绪数据的DataFrame
price_data: 包含价格数据的DataFrame
"""
self.sentiment_data = sentiment_data
self.price_data = price_data
self.composite_indicator = None
def preprocess_data(self) -> Tuple[pd.DataFrame, pd.DataFrame]:
"""
数据预处理，对情绪数据和价格数据进行清洗、对齐等操作
返回:
处理后的情绪数据和价格数据
"""
# TODO: 实现数据预处理逻辑
pass
def calculate_composite_indicator(self) -> pd.Series:
"""
计算复合情绪指标
返回:
复合情绪指标序列
"""
# TODO: 实现复合情绪指标计算逻辑
pass
def detect_extreme_values(self, window: int = 30) -> pd.DataFrame:
"""
检测情绪指标的极值点
参数:
window: 滑动窗口大小，用于确定局部极值
返回:
包含极值点信息的DataFrame
"""
# TODO: 实现极值检测逻辑
pass
def detect_reversals(self, threshold: float = 0.2) -> pd.DataFrame:
"""
检测情绪反转点
参数:
threshold: 情绪变化阈值，超过该阈值视为反转
返回:
包含反转点信息的DataFrame
"""
# TODO: 实现情绪反转检测逻辑
pass
def detect_divergence(self) -> pd.DataFrame:
"""
检测情绪与价格的背离情况
返回:
包含背离信息的DataFrame
"""
# TODO: 实现背离检测逻辑
pass
class MarketSentimentAnalyzer:
"""市场情绪与价格关联分析器"""
def __init__(self, sentiment_indicator: SentimentIndicator):
"""
初始化市场情绪分析器
参数:
sentiment_indicator: 情绪指标计算器实例
"""
self.sentiment_indicator = sentiment_indicator
self.sentiment_data = sentiment_indicator.sentiment_data
self.price_data = sentiment_indicator.price_data
self.composite_indicator = sentiment_indicator.composite_indicator
def analyze_lag_relationship(self, max_lag: int = 10) -> Dict[str, pd.DataFrame]:
"""
分析情绪指标与价格走势的滞后关系
参数:
max_lag: 最大滞后期数
返回:
不同滞后期的相关性结果
"""
# TODO: 实现滞后关系分析逻辑
pass
def calculate_correlation(self, method: str = 'pearson') -> pd.DataFrame:
"""
计算情绪指标与价格的相关性
参数:
method: 相关系数计算方法，可选'pearson', 'spearman', 'kendall'
返回:
相关性分析结果
"""
# TODO: 实现相关性计算逻辑
pass
def test_granger_causality(self, max_lag: int = 10) -> pd.DataFrame:
"""
进行格兰杰因果关系检验
参数:
max_lag: 最大滞后期数
返回:
格兰杰因果关系检验结果
"""
# TODO: 实现格兰杰因果关系检验逻辑
pass
def segment_market_conditions(self, window: int = 30) -> pd.DataFrame:
"""
根据价格波动性、趋势等将市场分割为不同条件
参数:
window: 用于计算市场条件的窗口大小
返回:
标记了不同市场条件的DataFrame
"""
# TODO: 实现市场条件分割逻辑
pass
def evaluate_indicator_by_market_condition(self) -> pd.DataFrame:
"""
评估情绪指标在不同市场条件下的有效性
返回:
不同市场条件下的情绪指标评估结果
"""
# TODO: 实现情绪指标评估逻辑
pass
def visualize_relationship(self, start_date: Optional[str] = None, end_date: Optional[str] = None):
"""
可视化情绪指标与价格走势的关系
参数:
start_date: 起始日期
end_date: 结束日期
"""
# TODO: 实现可视化逻辑
pass
class SentimentTradingStrategy:
"""基于情绪指标的交易策略框架"""
def __init__(self, analyzer: MarketSentimentAnalyzer):
"""
初始化情绪交易策略
参数:
analyzer: 市场情绪分析器实例
"""
self.analyzer = analyzer
self.position = 0 # 0表示空仓，1表示多头，-1表示空头
self.signals = pd.DataFrame()
def generate_signals(self, extreme_threshold: float = 0.8, reversal_threshold: float = 0.3) -> pd.DataFrame:
"""
基于情绪指标生成交易信号
参数:
extreme_threshold: 情绪极值阈值
reversal_threshold: 情绪反转阈值
返回:
包含交易信号的DataFrame
"""
# TODO: 实现信号生成逻辑
pass
def backtest(self, initial_capital: float = 10000.0) -> pd.DataFrame:
"""
回测策略表现
参数:
initial_capital: 初始资金
返回:
回测结果
"""
# TODO: 实现回测逻辑
pass
def calculate_performance_metrics(self) -> Dict[str, float]:
"""
计算策略性能指标
返回:
包含各种性能指标的字典
"""
# TODO: 实现性能指标计算逻辑
pass
# 示例使用代码
def main():
# 模拟加载数据
sentiment_data = pd.DataFrame({
'date': pd.date_range(start='2020-01-01', periods=500),
'positive_score': np.random.random(500),
'negative_score': np.random.random(500),
'neutral_score': np.random.random(500)
})
price_data = pd.DataFrame({
'date': pd.date_range(start='2020-01-01', periods=500),
'open': np.random.random(500) * 100 + 100,
'high': np.random.random(500) * 100 + 110,
'low': np.random.random(500) * 100 + 90,
'close': np.random.random(500) * 100 + 100,
'volume': np.random.random(500) * 1000000
})
# 设置日期为索引
sentiment_data.set_index('date', inplace=True)
price_data.set_index('date', inplace=True)
# 创建实例并执行分析
sentiment_indicator = SentimentIndicator(sentiment_data, price_data)
sentiment_indicator.preprocess_data()
sentiment_indicator.calculate_composite_indicator()
analyzer = MarketSentimentAnalyzer(sentiment_indicator)
lag_analysis = analyzer.analyze_lag_relationship()
correlation = analyzer.calculate_correlation()
causality = analyzer.test_granger_causality()
# 基于分析结果进行策略测试
strategy = SentimentTradingStrategy(analyzer)
signals = strategy.generate_signals()
backtest_results = strategy.backtest()
performance = strategy.calculate_performance_metrics()
# 输出关键结果
print("相关性分析结果:", correlation)
print("策略性能指标:", performance)
# 可视化分析
analyzer.visualize_relationship()
if __name__ == "__main__":
main()将情绪分析整合到交易策略中，设计基于情绪的交易信号生成机制。
关键任务：
import pandas as pd
import numpy as np
from typing import Dict, List, Tuple, Optional, Union
import logging
# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class EmotionBasedSignalGenerator:
"""
基于情绪的交易信号生成器
将市场情绪数据与技术指标结合，生成交易信号
"""
def __init__(self,
sentiment_threshold: Dict[str, float] = None,
signal_confirmation_window: int = 3,
use_technical_confirmation: bool = True):
"""
初始化信号生成器
参数:
sentiment_threshold: 情绪阈值字典，例如 {'bullish': 0.7, 'bearish': -0.6}
signal_confirmation_window: 信号确认窗口大小
use_technical_confirmation: 是否使用技术指标进行确认
"""
self.sentiment_threshold = sentiment_threshold or {'bullish': 0.6, 'bearish': -0.6}
self.signal_confirmation_window = signal_confirmation_window
self.use_technical_confirmation = use_technical_confirmation
# 用于存储历史信号
self.signal_history = []
def calculate_sentiment_signal(self, sentiment_score: float) -> str:
"""
基于情绪分数计算初步信号
参数:
sentiment_score: 情绪分数，通常在-1到1之间
返回:
信号字符串: 'buy', 'sell', 或 'neutral'
"""
# TODO: 学习者实现情绪阈值判断逻辑
# 提示: 使用self.sentiment_threshold来确定何时生成buy或sell信号
pass
def calculate_signal_strength(self,
sentiment_score: float,
technical_indicators: Dict[str, float] = None) -> float:
"""
计算信号强度，结合情绪和技术指标
参数:
sentiment_score: 情绪分数
technical_indicators: 技术指标字典，例如 {'rsi': 70, 'macd': 0.5}
返回:
信号强度，范围从0到1
"""
# TODO: 学习者实现信号强度计算逻辑
# 提示: 可以考虑情绪分数的绝对值，与技术指标的一致性等
pass
def confirm_signal(self,
preliminary_signal: str,
price_data: pd.DataFrame,
sentiment_data: pd.DataFrame,
technical_data: pd.DataFrame = None) -> bool:
"""
对初步信号进行确认
参数:
preliminary_signal: 初步信号，'buy'或'sell'
price_data: 价格数据
sentiment_data: 情绪数据
technical_data: 技术指标数据
返回:
布尔值，表示信号是否被确认
"""
# TODO: 学习者实现信号确认逻辑
# 提示: 可以检查情绪的持续性，或与技术指标的一致性
pass
def apply_filter(self,
signal: str,
signal_strength: float,
market_conditions: Dict[str, any]) -> Tuple[str, float]:
"""
应用过滤器调整信号和信号强度
参数:
signal: 原始信号
signal_strength: 原始信号强度
market_conditions: 市场条件，包括波动性、交易量等
返回:
调整后的信号和信号强度
"""
# TODO: 学习者实现信号过滤逻辑
# 提示: 可以基于市场条件调整信号，如高波动市场可能需要更强的信号确认
pass
def calculate_stop_levels(self,
entry_price: float,
signal: str,
signal_strength: float,
volatility: float) -> Dict[str, float]:
"""
基于情绪和信号强度计算止损止盈水平
参数:
entry_price: 入场价格
signal: 信号类型
signal_strength: 信号强度
volatility: 市场波动率
返回:
包含止损和止盈价格的字典
"""
# TODO: 学习者实现止损止盈计算逻辑
# 提示: 信号强度越高，止盈可以设置得更远；波动性也应该考虑进去
pass
def generate_signals(self,
price_data: pd.DataFrame,
sentiment_data: pd.DataFrame,
technical_data: pd.DataFrame = None) -> pd.DataFrame:
"""
生成交易信号主函数
参数:
price_data: 包含OHLCV数据的DataFrame
sentiment_data: 包含情绪数据的DataFrame
technical_data: 包含技术指标的DataFrame
返回:
包含交易信号的DataFrame
"""
signals = pd.DataFrame(index=price_data.index)
signals['signal'] = 'neutral'
signals['strength'] = 0.0
signals['stop_loss'] = np.nan
signals['take_profit'] = np.nan
# TODO: 学习者实现信号生成的完整流程
# 提示: 遍历数据，计算初步信号，确认信号，计算信号强度和止损止盈
return signals
def visualize_signals(self,
price_data: pd.DataFrame,
signals: pd.DataFrame,
sentiment_data: pd.DataFrame = None) -> None:
"""
可视化交易信号
参数:
price_data: 价格数据
signals: 信号数据
sentiment_data: 情绪数据
"""
# TODO: 学习者实现可视化逻辑
# 提示: 可以使用matplotlib或plotly绘制带有买卖点的价格图表
pass
class EmotionalTradingStrategy:
"""
情绪化交易策略
整合情绪信号生成器和交易执行逻辑
"""
def __init__(self,
signal_generator: EmotionBasedSignalGenerator,
initial_capital: float = 10000,
position_sizing_method: str = 'fixed',
risk_per_trade: float = 0.02):
"""
初始化交易策略
参数:
signal_generator: 情绪信号生成器
initial_capital: 初始资金
position_sizing_method: 仓位大小方法，'fixed'或'risk_based'
risk_per_trade: 每笔交易风险比例
"""
self.signal_generator = signal_generator
self.capital = initial_capital
self.position_sizing_method = position_sizing_method
self.risk_per_trade = risk_per_trade
self.positions = []
self.trade_history = []
def calculate_position_size(self,
signal: str,
signal_strength: float,
entry_price: float,
stop_loss: float) -> int:
"""
计算仓位大小
参数:
signal: 交易信号
signal_strength: 信号强度
entry_price: 入场价格
stop_loss: 止损价格
返回:
交易数量
"""
# TODO: 学习者实现仓位大小计算逻辑
# 提示: 可以基于风险、信号强度和资金量计算
pass
def execute_trades(self,
price_data: pd.DataFrame,
sentiment_data: pd.DataFrame,
technical_data: pd.DataFrame = None) -> pd.DataFrame:
"""
执行交易
参数:
price_data: 价格数据
sentiment_data: 情绪数据
technical_data: 技术指标数据
返回:
交易结果摘要
"""
# 生成信号
signals = self.signal_generator.generate_signals(
price_data, sentiment_data, technical_data
)
# TODO: 学习者实现交易执行逻辑
# 提示: 遍历信号数据，执行买卖操作，计算收益等
# 返回交易结果摘要
return pd.DataFrame()
def evaluate_performance(self) -> Dict[str, float]:
"""
评估策略表现
返回:
包含各种性能指标的字典
"""
# TODO: 学习者实现性能评估逻辑
# 提示: 计算夏普比率、最大回撤、胜率等指标
pass
# 使用示例
def example_usage():
# 准备数据（这里只是示例，学习者需要使用实际数据）
dates = pd.date_range(start='2023-01-01', periods=100, freq='D')
price_data = pd.DataFrame({
'open': np.random.normal(100, 2, 100),
'high': np.random.normal(102, 2, 100),
'low': np.random.normal(98, 2, 100),
'close': np.random.normal(101, 2, 100),
'volume': np.random.normal(1000000, 200000, 100)
}, index=dates)
sentiment_data = pd.DataFrame({
'sentiment_score': np.random.normal(0, 0.5, 100),
'sentiment_volume': np.random.normal(5000, 1000, 100)
}, index=dates)
technical_data = pd.DataFrame({
'rsi': np.random.normal(50, 15, 100),
'macd': np.random.normal(0, 0.5, 100),
'ma_50': np.random.normal(100, 1, 100)
}, index=dates)
# 创建信号生成器
signal_generator = EmotionBasedSignalGenerator(
sentiment_threshold={'bullish': 0.7, 'bearish': -0.6},
signal_confirmation_window=3,
use_technical_confirmation=True
)
# 创建交易策略
strategy = EmotionalTradingStrategy(
signal_generator=signal_generator,
initial_capital=10000,
position_sizing_method='risk_based',
risk_per_trade=0.02
)
# 执行交易
results = strategy.execute_trades(price_data, sentiment_data, technical_data)
# 评估性能
performance = strategy.evaluate_performance()
# 打印结果
print("交易结果摘要:")
print(results)
print("\n策略性能:")
for metric, value in performance.items():
print(f"{metric}: {value}")
if __name__ == "__main__":
# 运行示例
example_usage()优化交易策略并实现市场适应能力，提高回测和实盘表现。
关键任务：
import numpy as np
import pandas as pd
from abc import ABC, abstractmethod
from typing import Dict, List, Tuple, Any, Optional
import matplotlib.pyplot as plt
from datetime import datetime
import logging
# 配置日志
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
filename='strategy_optimization.log'
)
logger = logging.getLogger('strategy_optimizer')
class MarketState(ABC):
"""市场状态抽象基类"""
@abstractmethod
def identify(self, market_data: pd.DataFrame) -> str:
"""识别当前市场状态"""
pass
@abstractmethod
def get_optimal_parameters(self) -> Dict[str, Any]:
"""获取当前市场状态下的最优参数"""
pass
class DefaultMarketState(MarketState):
"""默认市场状态实现"""
def identify(self, market_data: pd.DataFrame) -> str:
# TODO: 实现市场状态识别逻辑
# 示例：可以通过波动率、趋势强度、成交量等指标识别市场状态
# 返回："trending_up", "trending_down", "ranging", "high_volatility" 等
return "undefined"
def get_optimal_parameters(self) -> Dict[str, Any]:
# TODO: 实现获取最优参数的逻辑
return {"default_param": 0.5}
class SentimentIndicator:
"""情绪指标计算和权重管理"""
def __init__(self, indicators: List[str], initial_weights: Optional[List[float]] = None):
self.indicators = indicators
if initial_weights is None:
# 平均分配权重
self.weights = [1.0/len(indicators)] * len(indicators)
else:
assert len(indicators) == len(initial_weights), "指标和权重数量必须匹配"
assert sum(initial_weights) == 1.0, "权重之和必须为1"
self.weights = initial_weights
self.historical_weights = []
def calculate_sentiment(self, data: pd.DataFrame) -> float:
# TODO: 实现情绪指标计算逻辑
# 示例：加权平均各个情绪指标
return 0.0 # 返回-1到1之间的值，表示情绪
def adjust_weights(self, performance_data: Dict[str, float]):
# TODO: 根据各指标表现调整权重
# 示例：根据预测准确度调整权重
self.historical_weights.append(self.weights.copy())
logger.info(f"Adjusted weights: {self.weights}")
class StrategyOptimizer:
"""策略参数优化和适应机制"""
def __init__(self, param_ranges: Dict[str, Tuple[float, float, float]]):
"""
初始化优化器
param_ranges: 参数范围字典，格式为 {参数名: (最小值, 最大值, 步长)}
"""
self.param_ranges = param_ranges
self.best_params = {}
self.performance_history = []
self.market_state_handler = DefaultMarketState()
def optimize(self, historical_data: pd.DataFrame, target_metric: str = 'sharpe_ratio') -> Dict[str, Any]:
"""
优化策略参数
historical_data: 历史数据
target_metric: 优化目标指标
"""
# TODO: 实现参数优化逻辑
# 可以使用网格搜索、遗传算法、贝叶斯优化等方法
self.best_params = {param: ranges[0] for param, ranges in self.param_ranges.items()}
return self.best_params
def adapt_to_market(self, current_data: pd.DataFrame) -> Dict[str, Any]:
"""
根据当前市场状态调整策略参数
current_data: 当前市场数据
"""
market_state = self.market_state_handler.identify(current_data)
logger.info(f"Current market state: {market_state}")
# TODO: 实现基于市场状态的参数调整逻辑
adapted_params = self.best_params.copy()
return adapted_params
class PerformanceMonitor:
"""策略性能评估与监控"""
def __init__(self):
self.metrics = {}
self.performance_history = []
def calculate_metrics(self, returns: pd.Series) -> Dict[str, float]:
"""
计算策略性能指标
returns: 策略收益率序列
"""
metrics = {}
# TODO: 实现各类性能指标计算
# 示例指标：
metrics['total_return'] = ((1 + returns).prod() - 1) * 100 # 总收益率(%)
metrics['annual_return'] = 0 # 年化收益率
metrics['sharpe_ratio'] = 0 # 夏普比率
metrics['max_drawdown'] = 0 # 最大回撤
metrics['win_rate'] = 0 # 胜率
self.metrics = metrics
self.performance_history.append(metrics)
return metrics
def plot_performance(self, returns: pd.Series):
"""生成性能报告图表"""
# TODO: 实现性能可视化逻辑
plt.figure(figsize=(12, 8))
# 示例: 绘制累计收益曲线
cumulative_returns = (1 + returns).cumprod() - 1
plt.plot(cumulative_returns)
plt.title('Cumulative Returns')
plt.ylabel('Return')
plt.grid(True)
plt.savefig('performance_report.png')
def generate_report(self) -> str:
"""生成性能报告"""
report = "===== 策略性能报告 =====\n"
for name, value in self.metrics.items():
report += f"{name}: {value:.4f}\n"
return report
class TradingStrategy:
"""交易策略主类"""
def __init__(self, initial_parameters: Dict[str, Any] = None):
self.parameters = initial_parameters or {}
self.optimizer = StrategyOptimizer({
'param1': (0.1, 0.9, 0.1), # (最小值, 最大值, 步长)
'param2': (10, 100, 10),
'param3': (5, 50, 5),
})
self.performance_monitor = PerformanceMonitor()
self.sentiment_indicator = SentimentIndicator(
indicators=['vix', 'put_call_ratio', 'bullish_percentage']
)
def train(self, historical_data: pd.DataFrame):
"""训练策略"""
logger.info("开始训练策略...")
# 优化参数
optimized_params = self.optimizer.optimize(historical_data)
self.parameters.update(optimized_params)
logger.info(f"优化后的参数: {self.parameters}")
def backtest(self, historical_data: pd.DataFrame) -> pd.Series:
"""回测策略"""
logger.info("开始回测策略...")
# TODO: 实现回测逻辑
# 这里需要实现具体的交易信号生成和持仓管理
# 示例代码生成模拟收益序列
returns = pd.Series(np.random.normal(0.001, 0.01, len(historical_data)), index=historical_data.index)
# 评估性能
metrics = self.performance_monitor.calculate_metrics(returns)
logger.info(f"回测性能指标: {metrics}")
return returns
def execute_trade(self, current_data: pd.DataFrame) -> Dict[str, Any]:
"""执行交易"""
# 适应当前市场状态
adapted_params = self.optimizer.adapt_to_market(current_data)
self.parameters.update(adapted_params)
# 计算当前情绪指标
sentiment = self.sentiment_indicator.calculate_sentiment(current_data)
# TODO: 实现交易决策逻辑
# 根据策略参数和情绪指标生成交易信号
return {"action": "hold", "position_size": 0, "sentiment": sentiment}
# 示例使用代码
if __name__ == "__main__":
# 模拟数据
dates = pd.date_range(start='2022-01-01', end='2023-01-01', freq='D')
data = pd.DataFrame({
'open': np.random.normal(100, 5, len(dates)),
'high': np.random.normal(102, 5, len(dates)),
'low': np.random.normal(98, 5, len(dates)),
'close': np.random.normal(101, 5, len(dates)),
'volume': np.random.normal(1000000, 200000, len(dates))
}, index=dates)
# 划分训练集和测试集
train_data = data[:'2022-09-30']
test_data = data['2022-10-01':]
# 创建策略实例
strategy = TradingStrategy()
# 训练策略
strategy.train(train_data)
# 回测策略
returns = strategy.backtest(test_data)
# 生成性能报告
strategy.performance_monitor.plot_performance(returns)
report = strategy.performance_monitor.generate_report()
print(report)
# 模拟实时交易
current_data = test_data.iloc[-10:].copy()
trade_decision = strategy.execute_trade(current_data)
print(f"交易决策: {trade_decision}")数据采集
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pandas: 数据处理与分析numpy: 数值计算matplotlib/seaborn: 数据可视化plotly: 交互式数据可视化streamlit: 快速创建数据应用程序回测与交易
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文本分析与NLP
情绪分析与市场预测
量化交易策略设计
策略优化与性能评估