小资金投资介绍(小资金投资可信吗)

导语：专为小资金投资者设计的交易策略及Python实现

专为小资金推荐的交易策略，并附Python代码，分别定义为函数，可以根据需要调用：

1.均值回归交易

均值回归交易基于价格最终会恢复到历史平均水平的想法。该策略涉及识别当前超买或超卖的资产，然后在相反方向建立头寸，预期回归均值。

import numpy as npimport pandas as pdfrom scipy.stats import zscoredef mean_reversion_strategy(prices, lookback=20, z_score_threshold=2):    rolling_mean = prices.rolling(window=lookback).mean()    z_scores = zscore(prices - rolling_mean)    buy_signals = z_scores <= -z_score_threshold    sell_signals = z_scores >= z_score_threshold    return buy_signals, sell_signals

2. 动量交易

动量交易涉及买入表现优于大盘的资产和卖出表现不佳的资产，基于价格趋势将持续的假设。

def momentum_strategy(prices, lookback=12, holding_period=1):    returns = prices.pct_change()    momentum = returns.rolling(window=lookback).mean()    ranking = momentum.rank(axis=1, ascending=False)        buy_signals = (ranking <= holding_period)    sell_signals = (ranking > holding_period)    return buy_signals, sell_signals

3. 配对交易

配对交易是一种市场中性策略，涉及识别两种历史上相关的资产，然后在它们的价格关系出现分歧时，在一种资产中持有多头头寸，在另一种资产中持有空头头寸。

def pairs_trading_strategy(prices, asset1, asset2, lookback=20, z_score_threshold=2):    spread = prices[asset1] - prices[asset2]    rolling_mean = spread.rolling(window=lookback).mean()    rolling_std = spread.rolling(window=lookback).std()    z_scores = (spread - rolling_mean) / rolling_std        buy_signals = z_scores <= -z_score_threshold    sell_signals = z_scores >= z_score_threshold    return buy_signals, sell_signals

4.突破交易

突破交易涉及识别资产突破整合模式并进入新趋势的价格水平。一旦出现突破，交易者就会在突破的方向建仓，预计趋势会延续。

def breakout_strategy(prices, lookback=20):    rolling_high = prices.rolling(window=lookback).max()    rolling_low = prices.rolling(window=lookback).min()        buy_signals = (prices > rolling_high.shift(1))    sell_signals = (prices < rolling_low.shift(1))    return buy_signals, sell_signals

这些交易策略在实际交易实施之前应进行回测评估。

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