<\/p>\n
\u6307\u6578\u52a0\u6b0a\u79fb\u52d5\u5e73\u5747\uff08EWMA\uff09\u662f\u4e00\u7a2e\u5ee3\u6cdb\u4f7f\u7528\u7684\u6280\u8853\u5206\u6790\u6307\u6a19\uff0c\u80fd\u5920\u5e6b\u52a9\u6295\u8cc7\u8005\u6df1\u5165\u4e86\u89e3\u80a1\u7968\u5e02\u5834\u7684\u8da8\u52e2\u3002\u76f8\u8f03\u65bc\u50b3\u7d71\u7684\u7c21\u55ae\u79fb\u52d5\u5e73\u5747\uff08SMA\uff09\uff0cEWMA\u66f4\u80fd\u654f\u92b3\u5730\u53cd\u6620\u6700\u8fd1\u7684\u80a1\u50f9\u8b8a\u5316\uff0c\u4e26\u6709\u6548\u6355\u6349\u5e02\u5834\u8da8\u52e2\u3002<\/p>\n
### EWMA\u7684\u4f7f\u7528\u6642\u6a5f
\n\u7576\u6295\u8cc7\u8005\u5e0c\u671b\u7372\u5f97\u66f4\u7cbe\u78ba\u7684\u5e02\u5834\u8da8\u52e2\u5224\u65b7\u6642\uff0cEWMA\u662f\u4e00\u500b\u6975\u4f73\u7684\u9078\u64c7\u3002\u5b83\u901a\u904e\u7d66\u4e88\u6700\u8fd1\u7684\u6578\u64da\u66f4\u9ad8\u7684\u6b0a\u91cd\uff0c\u5e6b\u52a9\u6295\u8cc7\u8005\u66f4\u597d\u5730\u628a\u63e1\u6295\u8cc7\u6a5f\u6703\u3002<\/p>\n
### EWMA\u7684\u8a08\u7b97\u65b9\u5f0f
\n\u4ee5\u4e0b\u662fEWMA\u7684\u8a08\u7b97\u51fd\u6578\uff0c\u9019\u6bb5\u4ee3\u78bc\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528Python\u8a08\u7b97EWMA\uff1a<\/p>\n
“`python
\nimport numpy as np<\/p>\n
# EWMA\u8a08\u7b97\u51fd\u6578
\ndef ewma(data, window):
\n alpha = 2 \/ (window + 1.0)
\n alpha_rev = 1 – alpha
\n n = data.shape[0]\n pows = alpha_rev ** (np.arange(n + 1))
\n scale_arr = 1 \/ pows[:-1]\n offset = data[0] * pows[1:]\n pw0 = alpha * alpha_rev ** (n – 1)
\n mult = data * pw0 * scale_arr
\n cumsums = mult.cumsum()
\n out = offset + cumsums * scale_arr[::-1]\n return out
\n“`<\/p>\n
### Python\u7bc4\u4f8b\u7a0b\u5f0f\u78bc
\n\u4ee5\u4e0b\u662f\u5229\u7528\u4e0a\u8ff0\u51fd\u6578\u8a08\u7b97EWMA\u4e26\u7e6a\u88fd\u5716\u5f62\u7684\u5b8c\u6574\u7bc4\u4f8b\uff1a<\/p>\n
“`python
\nimport numpy as np
\nimport matplotlib.pyplot as plt<\/p>\n
# \u751f\u6210\u5047\u8cc7\u6599
\ndata = np.random.randn(1000)<\/p>\n
# \u8a08\u7b97EWMA
\newma_data = ewma(data, 10)<\/p>\n
# \u7e6a\u88fd\u8cc7\u6599
\nplt.figure(figsize=(14, 7))
\nplt.plot(data, label=’\u539f\u59cb\u8cc7\u6599’, alpha=0.5)
\nplt.plot(ewma_data, label=’EWMA\u8cc7\u6599’, color=’orange’)
\nplt.title(‘\u6307\u6578\u52a0\u6b0a\u79fb\u52d5\u5e73\u5747(EWMA)\u793a\u4f8b’)
\nplt.xlabel(‘\u6642\u9593’)
\nplt.ylabel(‘\u503c’)
\nplt.legend()
\nplt.grid()
\nplt.show()
\n“`<\/p>\n
### EWMA\u7684\u512a\u9ede\u8207\u7f3a\u9ede
\n#### \u512a\u9ede
\n– **\u53cd\u6620\u6700\u8fd1\u8b8a\u5316**\uff1aEWMA\u80fd\u5920\u66f4\u597d\u5730\u53cd\u6620\u6700\u8fd1\u80a1\u50f9\u7684\u8b8a\u5316\uff0c\u56e0\u70ba\u5b83\u5c07\u66f4\u591a\u7684\u6b0a\u91cd\u653e\u5728\u6700\u65b0\u7684\u6578\u64da\u4e0a\u3002
\n– **\u6355\u6349\u8da8\u52e2**\uff1a\u900f\u904e\u52a0\u6b0a\u6a5f\u5236\uff0cEWMA\u80fd\u8fc5\u901f\u6355\u6349\u5230\u5e02\u5834\u7684\u8b8a\u5316\u8da8\u52e2\u3002
\n– **\u6613\u65bc\u8a08\u7b97**\uff1a\u76f8\u8f03\u65bc\u5176\u4ed6\u6280\u8853\u6307\u6a19\uff0cEWMA\u7684\u8a08\u7b97\u76f8\u5c0d\u7c21\u55ae\uff0c\u53ea\u9700\u8a2d\u5b9a\u4e00\u500b\u7a97\u53e3\u5927\u5c0f\u53c3\u6578\u3002<\/p>\n
#### \u7f3a\u9ede
\n– **\u5ef6\u9072\u53cd\u61c9**\uff1a\u7531\u65bc\u8a08\u7b97\u516c\u5f0f\u7684\u7279\u6027\uff0cEWMA\u53ef\u80fd\u6703\u5c0d\u5e02\u5834\u8b8a\u5316\u53cd\u61c9\u8f03\u6162\u3002
\n– **\u504f\u5dee\u554f\u984c**\uff1a\u5728\u5e02\u5834\u51fa\u73fe\u5287\u70c8\u8b8a\u5316\u6642\uff0cEWMA\u53ef\u80fd\u6703\u986f\u793a\u51fa\u4e00\u5b9a\u7684\u504f\u5dee\u3002
\n– **\u8a08\u7b97\u8aa4\u5dee**\uff1a\u5728\u6578\u64da\u91cf\u8f03\u5c0f\u7684\u60c5\u6cc1\u4e0b\uff0c\u8a08\u7b97\u53ef\u80fd\u6703\u53d7\u5230\u8aa4\u5dee\u7684\u5f71\u97ff\u3002<\/p>\n
\u7e3d\u7684\u4f86\u8aaa\uff0cEWMA\u662f\u4e00\u500b\u5f37\u5927\u7684\u6280\u8853\u5206\u6790\u5de5\u5177\uff0c\u80fd\u5920\u5e6b\u52a9\u6295\u8cc7\u8005\u66f4\u597d\u5730\u7406\u89e3\u5e02\u5834\u8da8\u52e2\uff0c\u4e26\u6293\u4f4f\u6295\u8cc7\u6a5f\u6703\u3002\u5c0d\u65bc\u60f3\u8981\u6df1\u5165\u7814\u7a76\u7684\u8b80\u8005\uff0c\u53ef\u4ee5\u53c3\u8003\u66f4\u591a[Python\u6280\u8853\u5206\u6790\u76f8\u95dc\u8cc7\u6599](https:\/\/vocus.cc)\u4f86\u64f4\u5c55\u81ea\u5df1\u7684\u77e5\u8b58\u3002<\/p>\n
### Q&A\uff08\u5e38\u898b\u554f\u984c\u89e3\u7b54\uff09
\n**Q: EWMA\u548cSMA\u6709\u4ec0\u9ebc\u4e3b\u8981\u5340\u5225\uff1f**
\nA: EWMA\u5c0d\u6700\u8fd1\u7684\u6578\u64da\u8ce6\u4e88\u66f4\u9ad8\u7684\u6b0a\u91cd\uff0c\u800cSMA\u5247\u662f\u5c0d\u6240\u6709\u6578\u64da\u8ce6\u4e88\u76f8\u540c\u7684\u6b0a\u91cd\uff0c\u9019\u4f7f\u5f97EWMA\u80fd\u66f4\u5feb\u53cd\u6620\u5e02\u5834\u8b8a\u5316\u3002<\/p>\n
**Q: \u5982\u4f55\u9078\u64c7EWMA\u7684\u7a97\u53e3\u5927\u5c0f\uff1f**
\nA: \u7a97\u53e3\u5927\u5c0f\u7684\u9078\u64c7\u901a\u5e38\u53d6\u6c7a\u65bc\u4ea4\u6613\u7b56\u7565\u548c\u5e02\u5834\u7684\u6ce2\u52d5\u6027\uff0c\u5efa\u8b70\u6839\u64da\u6b77\u53f2\u8cc7\u6599\u9032\u884c\u6e2c\u8a66\u4f86\u627e\u5230\u6700\u4f73\u7684\u7a97\u53e3\u5927\u5c0f\u3002<\/p>\n
**Q: EWMA\u662f\u5426\u9069\u7528\u65bc\u6240\u6709\u985e\u578b\u7684\u5e02\u5834\uff1f**
\nA: EWMA\u5728\u6ce2\u52d5\u8f03\u5927\u7684\u5e02\u5834\u4e2d\u8868\u73fe\u8f03\u597d\uff0c\u4f46\u5728\u5e73\u7a69\u5e02\u5834\u4e2d\u53ef\u80fd\u6703\u5c0e\u81f4\u4fe1\u865f\u5ef6\u9072\uff0c\u56e0\u6b64\u9700\u8981\u6839\u64da\u5177\u9ad4\u60c5\u6cc1\u8abf\u6574\u4f7f\u7528\u7b56\u7565\u3002<\/p>\n
—<\/p>\n","protected":false},"excerpt":{"rendered":"
\u6587\u7ae0\u6458\u8981\uff1a\u6307\u6578\u52a0\u6b0a\u79fb\u52d5\u5e73\u5747(EWMA)\u662f\u4e00\u7a2e\u5e38\u7528\u7684\u6280\u8853\u5206\u6790\u6307\u6a19\uff0c\u53ef\u4ee5\u5e6b\u52a9\u6295\u8cc7\u8005\u66f4\u597d\u5730\u5206\u6790\u80a1\u7968\u5e02\u5834\u7684\u8da8\u52e2\uff0c\u672c\u6587\u5c07\u8a73\u7d30\u4ecb\u7d39EWMA\u7684\u4f7f\u7528\u6642\u6a5f\u4ee5\u53caPython\u7bc4\u4f8b\uff0c\u8b93\u6295\u8cc7\u8005\u66f4\u52a0\u4e86\u89e3\u6b64\u6307\u6a19\u7684\u50f9\u503c\u3002<\/p>\n","protected":false},"author":1,"featured_media":2518,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[18,189],"tags":[15,188],"class_list":["post-6082","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","category-189","tag-python","tag-188"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/badgameshow.com\/steven\/wp-content\/uploads\/2022\/12\/DALL\u00b7E-2022-12-28-14.25.55-\u62f7\u8c9d2.png","jetpack-related-posts":[],"jetpack_shortlink":"https:\/\/wp.me\/pcFK27-1A6","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/badgameshow.com\/steven\/wp-json\/wp\/v2\/posts\/6082","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/badgameshow.com\/steven\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/badgameshow.com\/steven\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/badgameshow.com\/steven\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/badgameshow.com\/steven\/wp-json\/wp\/v2\/comments?post=6082"}],"version-history":[{"count":3,"href":"https:\/\/badgameshow.com\/steven\/wp-json\/wp\/v2\/posts\/6082\/revisions"}],"predecessor-version":[{"id":12757,"href":"https:\/\/badgameshow.com\/steven\/wp-json\/wp\/v2\/posts\/6082\/revisions\/12757"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/badgameshow.com\/steven\/wp-json\/wp\/v2\/media\/2518"}],"wp:attachment":[{"href":"https:\/\/badgameshow.com\/steven\/wp-json\/wp\/v2\/media?parent=6082"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/badgameshow.com\/steven\/wp-json\/wp\/v2\/categories?post=6082"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/badgameshow.com\/steven\/wp-json\/wp\/v2\/tags?post=6082"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}