[{"data":1,"prerenderedAt":1179},["ShallowReactive",2],{"doc-analytics/correlation-strength":3},{"id":4,"title":5,"appLink":6,"body":7,"category":1162,"description":1163,"extension":1164,"level":1165,"meta":1166,"navigation":152,"next":1167,"path":1168,"related":1169,"seo":1172,"stem":1173,"tags":1174,"__hash__":1178,"_path":1168},"docs/docs/analytics/correlation-strength.md","Understanding Correlation Strength (R-Values)","/analytics/opportunities",{"type":8,"value":9,"toc":1114},"minimark",[10,14,25,30,33,62,65,68,72,75,219,224,230,238,244,255,261,272,278,289,295,306,310,313,319,330,336,344,350,358,363,371,377,381,384,388,394,399,410,416,420,426,431,442,447,451,457,462,473,478,482,488,493,504,509,513,516,520,523,531,535,538,544,552,556,559,579,585,589,592,596,615,618,622,641,644,648,667,670,674,689,692,696,700,711,717,720,726,730,735,741,746,750,756,760,774,780,784,789,794,797,801,804,808,834,838,862,866,889,893,897,900,904,907,911,925,928,932,935,946,949,953,957,963,967,972,980,985,993,998,1006,1011,1019,1023,1030,1044,1050,1054,1059,1062,1067,1070,1075,1078,1083,1086,1091,1094,1098,1104,1110],[11,12,5],"h1",{"id":13},"understanding-correlation-strength-r-values",[15,16,17],"blockquote",{},[18,19,20,24],"p",{},[21,22,23],"strong",{},"Educational Resource:"," This guide explains statistical correlation analysis. These metrics describe historical relationships and do not predict future outcomes. All investments involve risk and past performance does not guarantee future results.",[26,27,29],"h2",{"id":28},"what-is-a-correlation","What is a Correlation?",[18,31,32],{},"A correlation measures how strongly two things move together in historical data. In trading, we use it to measure relationships like:",[34,35,36,46,54],"ul",{},[37,38,39,42,43],"li",{},[21,40,41],{},"Insider buying"," and ",[21,44,45],{},"future price increases",[37,47,48,42,51],{},[21,49,50],{},"8-K filings",[21,52,53],{},"volatility changes",[37,55,56,42,59],{},[21,57,58],{},"Momentum signals",[21,60,61],{},"continued trends",[18,63,64],{},"The correlation coefficient (called \"r\" or \"r-value\") tells you how reliable this relationship was historically.",[18,66,67],{},"Think of it like measuring how well two dancers move in sync: r = 1.0 means perfect synchronization, r = 0 means no coordination at all.",[26,69,71],{"id":70},"the-r-value-scale","The R-Value Scale",[18,73,74],{},"Correlation coefficients range from -1.0 to +1.0:",[76,77,82],"pre",{"className":78,"code":79,"language":80,"meta":81,"style":81},"language-mermaid shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","graph LR\n    A[\"\u003Cstrong>-1.0\u003C/strong>\u003Cbr/>Perfect Negative\u003Cbr/>❌ Never Happens\"]\n    B[\"-0.7\u003Cbr/>Very Strong\u003Cbr/>Mean Reversion\"]\n    C[\"-0.5\u003Cbr/>Strong\u003Cbr/>Contrarian Plays\"]\n    D[\"-0.3\u003Cbr/>Moderate\u003Cbr/>Weak Signal\"]\n    E[\"\u003Cstrong>0\u003C/strong>\u003Cbr/>No Correlation\u003Cbr/>❌ Filtered Out\"]\n    F[\"0.3\u003Cbr/>Moderate\u003Cbr/>Minimum Threshold\"]\n    G[\"0.5\u003Cbr/>Strong\u003Cbr/>Good Quality\"]\n    H[\"0.7\u003Cbr/>Very Strong\u003Cbr/>High Conviction\"]\n    I[\"\u003Cstrong>+1.0\u003C/strong>\u003Cbr/>Perfect Positive\u003Cbr/>❌ Never Happens\"]\n\n    A --> B --> C --> D --> E --> F --> G --> H --> I\n\n    style A fill:#ff6b6b\n    style B fill:#ffa94d\n    style C fill:#ffd93d\n    style D fill:#f0f0f0\n    style E fill:#ddd\n    style F fill:#b8e6b8\n    style G fill:#74c69d\n    style H fill:#40916c\n    style I fill:#2d6a4f\n","mermaid","",[83,84,85,93,99,105,111,117,123,129,135,141,147,154,160,165,171,177,183,189,195,201,207,213],"code",{"__ignoreMap":81},[86,87,90],"span",{"class":88,"line":89},"line",1,[86,91,92],{},"graph LR\n",[86,94,96],{"class":88,"line":95},2,[86,97,98],{},"    A[\"\u003Cstrong>-1.0\u003C/strong>\u003Cbr/>Perfect Negative\u003Cbr/>❌ Never Happens\"]\n",[86,100,102],{"class":88,"line":101},3,[86,103,104],{},"    B[\"-0.7\u003Cbr/>Very Strong\u003Cbr/>Mean Reversion\"]\n",[86,106,108],{"class":88,"line":107},4,[86,109,110],{},"    C[\"-0.5\u003Cbr/>Strong\u003Cbr/>Contrarian Plays\"]\n",[86,112,114],{"class":88,"line":113},5,[86,115,116],{},"    D[\"-0.3\u003Cbr/>Moderate\u003Cbr/>Weak Signal\"]\n",[86,118,120],{"class":88,"line":119},6,[86,121,122],{},"    E[\"\u003Cstrong>0\u003C/strong>\u003Cbr/>No Correlation\u003Cbr/>❌ Filtered Out\"]\n",[86,124,126],{"class":88,"line":125},7,[86,127,128],{},"    F[\"0.3\u003Cbr/>Moderate\u003Cbr/>Minimum Threshold\"]\n",[86,130,132],{"class":88,"line":131},8,[86,133,134],{},"    G[\"0.5\u003Cbr/>Strong\u003Cbr/>Good Quality\"]\n",[86,136,138],{"class":88,"line":137},9,[86,139,140],{},"    H[\"0.7\u003Cbr/>Very Strong\u003Cbr/>High Conviction\"]\n",[86,142,144],{"class":88,"line":143},10,[86,145,146],{},"    I[\"\u003Cstrong>+1.0\u003C/strong>\u003Cbr/>Perfect Positive\u003Cbr/>❌ Never Happens\"]\n",[86,148,150],{"class":88,"line":149},11,[86,151,153],{"emptyLinePlaceholder":152},true,"\n",[86,155,157],{"class":88,"line":156},12,[86,158,159],{},"    A --> B --> C --> D --> E --> F --> G --> H --> I\n",[86,161,163],{"class":88,"line":162},13,[86,164,153],{"emptyLinePlaceholder":152},[86,166,168],{"class":88,"line":167},14,[86,169,170],{},"    style A fill:#ff6b6b\n",[86,172,174],{"class":88,"line":173},15,[86,175,176],{},"    style B fill:#ffa94d\n",[86,178,180],{"class":88,"line":179},16,[86,181,182],{},"    style C fill:#ffd93d\n",[86,184,186],{"class":88,"line":185},17,[86,187,188],{},"    style D fill:#f0f0f0\n",[86,190,192],{"class":88,"line":191},18,[86,193,194],{},"    style E fill:#ddd\n",[86,196,198],{"class":88,"line":197},19,[86,199,200],{},"    style F fill:#b8e6b8\n",[86,202,204],{"class":88,"line":203},20,[86,205,206],{},"    style G fill:#74c69d\n",[86,208,210],{"class":88,"line":209},21,[86,211,212],{},"    style H fill:#40916c\n",[86,214,216],{"class":88,"line":215},22,[86,217,218],{},"    style I fill:#2d6a4f\n",[220,221,223],"h3",{"id":222},"positive-correlations","Positive Correlations",[18,225,226,229],{},[21,227,228],{},"r = +1.0:"," Perfect positive relationship",[34,231,232,235],{},[37,233,234],{},"When X increases, Y always increases by the same proportion",[37,236,237],{},"Essentially never happens in real markets",[18,239,240,243],{},[21,241,242],{},"r = +0.7 to +0.9:"," Very strong positive",[34,245,246,249,252],{},[37,247,248],{},"When X increases, Y tends to increase",[37,250,251],{},"Highly reliable relationship",[37,253,254],{},"What we look for in momentum patterns",[18,256,257,260],{},[21,258,259],{},"r = +0.5 to +0.7:"," Strong positive",[34,262,263,266,269],{},[37,264,265],{},"Clear upward relationship",[37,267,268],{},"Good quality signal",[37,270,271],{},"Acceptable for most strategies",[18,273,274,277],{},[21,275,276],{},"r = +0.3 to +0.5:"," Moderate positive",[34,279,280,283,286],{},[37,281,282],{},"Noticeable relationship",[37,284,285],{},"Requires additional confirmation",[37,287,288],{},"Minimum threshold for consideration",[18,290,291,294],{},[21,292,293],{},"r = 0 to +0.3:"," Weak positive",[34,296,297,300,303],{},[37,298,299],{},"Barely detectable relationship",[37,301,302],{},"Not actionable",[37,304,305],{},"We filter these out",[220,307,309],{"id":308},"negative-correlations","Negative Correlations",[18,311,312],{},"Negative correlations are just as useful - they indicate inverse relationships.",[18,314,315,318],{},[21,316,317],{},"r = -0.7 to -1.0:"," Very strong negative",[34,320,321,324,327],{},[37,322,323],{},"When X increases, Y decreases",[37,325,326],{},"Excellent for mean reversion strategies",[37,328,329],{},"\"Fade the move\" setups",[18,331,332,335],{},[21,333,334],{},"r = -0.5 to -0.7:"," Strong negative",[34,337,338,341],{},[37,339,340],{},"Clear inverse relationship",[37,342,343],{},"Good for contrarian plays",[18,345,346,349],{},[21,347,348],{},"r = -0.3 to -0.5:"," Moderate negative",[34,351,352,355],{},[37,353,354],{},"Noticeable inverse pattern",[37,356,357],{},"Use with additional signals",[18,359,360],{},[21,361,362],{},"Example of negative correlation:",[76,364,369],{"className":365,"code":367,"language":368},[366],"language-text","overbought_RSI → forward_return_5d (r=-0.62)\n","text",[83,370,367],{"__ignoreMap":81},[18,372,373,376],{},[21,374,375],{},"Translation:"," When RSI signals \"overbought,\" the stock tends to decline over the next 5 days. This is a fade signal, not a chase signal.",[26,378,380],{"id":379},"interpreting-r-values-in-drivers","Interpreting R-Values in Drivers",[18,382,383],{},"Let's decode real examples you'll see:",[220,385,387],{"id":386},"example-1-strong-positive-correlation","Example 1: Strong Positive Correlation",[76,389,392],{"className":390,"code":391,"language":368},[366],"insider_buying_20d → forward_return_30d\nr=0.74, n=156\n",[83,393,391],{"__ignoreMap":81},[18,395,396],{},[21,397,398],{},"What r=0.74 means:",[34,400,401,404,407],{},[37,402,403],{},"Very strong positive relationship",[37,405,406],{},"When insiders bought, stocks rose ~74% of the time historically",[37,408,409],{},"High-quality pattern worth your attention",[18,411,412,415],{},[21,413,414],{},"How to use it:","\nLook for opportunities where this driver contributes ≥20% to the total score. Combined with high sample size (n=156), this is a reliable signal.",[220,417,419],{"id":418},"example-2-moderate-positive-correlation","Example 2: Moderate Positive Correlation",[76,421,424],{"className":422,"code":423,"language":368},[366],"earnings_momentum → forward_return_20d\nr=0.42, n=203\n",[83,425,423],{"__ignoreMap":81},[18,427,428],{},[21,429,430],{},"What r=0.42 means:",[34,432,433,436,439],{},[37,434,435],{},"Moderate positive relationship",[37,437,438],{},"Pattern exists but not overwhelming",[37,440,441],{},"Needs confirmation from other drivers",[18,443,444,446],{},[21,445,414],{},"\nDon't trade this pattern alone. Look for 2-3 other drivers with similar direction to increase confidence.",[220,448,450],{"id":449},"example-3-strong-negative-correlation","Example 3: Strong Negative Correlation",[76,452,455],{"className":453,"code":454,"language":368},[366],"high_volatility_spike → forward_return_10d\nr=-0.58, n=134\n",[83,456,454],{"__ignoreMap":81},[18,458,459],{},[21,460,461],{},"What r=-0.58 means:",[34,463,464,467,470],{},[37,465,466],{},"Strong inverse relationship",[37,468,469],{},"After volatility spikes, stocks historically calmed down",[37,471,472],{},"Mean reversion pattern",[18,474,475,477],{},[21,476,414],{},"\nThis is a contrarian signal. When volatility explodes, the pattern suggests it won't last. Consider selling volatility (options strategies) or waiting for entry.",[220,479,481],{"id":480},"example-4-weak-correlation","Example 4: Weak Correlation",[76,483,486],{"className":484,"code":485,"language":368},[366],"volume_increase → forward_return_30d\nr=0.21, n=287\n",[83,487,485],{"__ignoreMap":81},[18,489,490],{},[21,491,492],{},"What r=0.21 means:",[34,494,495,498,501],{},[37,496,497],{},"Weak relationship",[37,499,500],{},"Volume alone doesn't predict much",[37,502,503],{},"Not actionable as standalone signal",[18,505,506,508],{},[21,507,414],{},"\nYou won't see this in our system - we filter correlations below r=0.3. If you see it in external research, ignore it unless combined with stronger signals.",[26,510,512],{"id":511},"what-makes-a-correlation-strong","What Makes a Correlation Strong?",[18,514,515],{},"R-value alone doesn't tell the whole story. Evaluate three factors together:",[220,517,519],{"id":518},"_1-absolute-strength-r","1. Absolute Strength (|r|)",[18,521,522],{},"Use absolute value to compare positive and negative correlations:",[34,524,525,528],{},[37,526,527],{},"|r| = 0.70 is equally strong whether r = +0.70 or r = -0.70",[37,529,530],{},"Direction matters for strategy, but strength is what matters for reliability",[220,532,534],{"id":533},"_2-sample-size-n","2. Sample Size (n)",[18,536,537],{},"A correlation of r=0.80 based on n=15 observations is less reliable than r=0.60 based on n=200 observations.",[18,539,540,543],{},[21,541,542],{},"Why?"," Small samples are prone to luck. Large samples prove the pattern is real.",[18,545,546,547],{},"Learn more: ",[548,549,551],"a",{"href":550},"/docs/analytics/sample-size-importance","Sample Size Importance",[220,553,555],{"id":554},"_3-statistical-significance-p-value","3. Statistical Significance (p-value)",[18,557,558],{},"The p-value tells you the probability the correlation happened by random chance.",[34,560,561,567,573],{},[37,562,563,566],{},[21,564,565],{},"p \u003C 0.05:"," Less than 5% chance it's random (we require this)",[37,568,569,572],{},[21,570,571],{},"p \u003C 0.01:"," Less than 1% chance it's random (very strong)",[37,574,575,578],{},[21,576,577],{},"p \u003C 0.001:"," Less than 0.1% chance it's random (extremely strong)",[18,580,546,581],{},[548,582,584],{"href":583},"/docs/advanced/statistical-significance","Statistical Significance",[26,586,588],{"id":587},"the-sweet-spot-r-values-we-look-for","The Sweet Spot: R-Values We Look For",[18,590,591],{},"Our quality standards for showing you patterns:",[220,593,595],{"id":594},"minimum-requirements","Minimum Requirements",[34,597,598,604,610],{},[37,599,600,603],{},[21,601,602],{},"|r| ≥ 0.3:"," Moderate correlation or better",[37,605,606,609],{},[21,607,608],{},"n ≥ 30:"," Minimum sample size",[37,611,612,614],{},[21,613,565],{}," Statistically significant",[18,616,617],{},"These are the bare minimums. Most patterns you see exceed these thresholds.",[220,619,621],{"id":620},"high-quality-patterns","High-Quality Patterns",[34,623,624,630,636],{},[37,625,626,629],{},[21,627,628],{},"|r| ≥ 0.6:"," Strong to very strong correlation",[37,631,632,635],{},[21,633,634],{},"n ≥ 100:"," Large, robust sample",[37,637,638,640],{},[21,639,571],{}," Highly significant",[18,642,643],{},"When you see these numbers, pay attention. These are well-tested, reliable patterns.",[220,645,647],{"id":646},"elite-patterns","Elite Patterns",[34,649,650,656,662],{},[37,651,652,655],{},[21,653,654],{},"|r| ≥ 0.7:"," Very strong correlation",[37,657,658,661],{},[21,659,660],{},"n ≥ 150:"," Very large sample",[37,663,664,666],{},[21,665,577],{}," Extremely significant",[18,668,669],{},"Rare but powerful. These are the highest-conviction setups in the system.",[220,671,673],{"id":672},"quality-standards-reference","Quality Standards Reference",[18,675,676,677,680,681,684,685,688],{},"| Quality Tier | |r| Threshold | Sample Size | p-value | Signal Interpretation |\n|--------------|---------------|-------------|---------|----------------------|\n| 🥉 ",[21,678,679],{},"Minimum"," | ≥ 0.3 | ≥ 30 | \u003C 0.05 | Emerging pattern - use with caution and additional confirmation |\n| 🥈 ",[21,682,683],{},"High-Quality"," | ≥ 0.6 | ≥ 100 | \u003C 0.01 | Reliable pattern - well-tested across many scenarios |\n| 🥇 ",[21,686,687],{},"Elite"," | ≥ 0.7 | ≥ 150 | \u003C 0.001 | Highest conviction - rare but powerful setups |",[18,690,691],{},"When evaluating drivers, match them against these tiers to quickly assess quality. Elite patterns deserve the most attention in your analysis.",[26,693,695],{"id":694},"common-mistakes-interpreting-r-values","Common Mistakes Interpreting R-Values",[220,697,699],{"id":698},"mistake-1-assuming-causation","Mistake 1: Assuming Causation",[18,701,702,705,706,710],{},[21,703,704],{},"Wrong interpretation:","\n\"r=0.70 means insider buying ",[707,708,709],"em",{},"causes"," the stock to rise\"",[18,712,713,716],{},[21,714,715],{},"Correct interpretation:","\n\"r=0.70 means historically, when insiders bought, stocks tended to rise. This could be because insiders have information, or because they buy when stocks are undervalued, or both.\"",[18,718,719],{},"Correlation ≠ causation. The pattern is real, but the mechanism might be complex.",[18,721,546,722],{},[548,723,725],{"href":724},"/docs/advanced/correlation-vs-causation","Correlation vs Causation",[220,727,729],{"id":728},"mistake-2-ignoring-sample-size","Mistake 2: Ignoring Sample Size",[18,731,732,734],{},[21,733,704],{},"\n\"r=0.85 is amazing, I'm going all-in\"",[18,736,737,740],{},[21,738,739],{},"Missing context:","\nWhat if n=12? That could be luck. Always check sample size.",[18,742,743,745],{},[21,744,715],{},"\n\"r=0.85 with n=200 is amazing. r=0.85 with n=12 needs more data.\"",[220,747,749],{"id":748},"mistake-3-comparing-apples-to-oranges","Mistake 3: Comparing Apples to Oranges",[18,751,752,755],{},[21,753,754],{},"Wrong comparison:","\n\"Pattern A has r=0.50 and Pattern B has r=0.65, so B is better\"",[18,757,758],{},[21,759,739],{},[34,761,762,765,768,771],{},[37,763,764],{},"What are the time horizons? (5-day vs 30-day returns)",[37,766,767],{},"What are the sample sizes?",[37,769,770],{},"What are the sectors?",[37,772,773],{},"What are the market conditions?",[18,775,776,779],{},[21,777,778],{},"Correct approach:","\nCompare patterns with similar characteristics (time horizon, sector, sample size) or evaluate each pattern independently against your strategy requirements.",[220,781,783],{"id":782},"mistake-4-treating-r-values-as-probabilities","Mistake 4: Treating R-Values as Probabilities",[18,785,786,788],{},[21,787,704],{},"\n\"r=0.70 means 70% win rate\"",[18,790,791,793],{},[21,792,715],{},"\n\"r=0.70 measures how consistently two variables move together, not win rate. Win rate requires different analysis (success rate in backtests).\"",[18,795,796],{},"R-value and success rate are related but different metrics. Check both.",[26,798,800],{"id":799},"r-values-across-different-time-horizons","R-Values Across Different Time Horizons",[18,802,803],{},"Correlations change based on the time period measured:",[220,805,807],{"id":806},"short-term-patterns-5-10-days","Short-Term Patterns (5-10 days)",[34,809,810,816,822,828],{},[37,811,812,815],{},[21,813,814],{},"Typical r-values:"," 0.35 - 0.55",[37,817,818,821],{},[21,819,820],{},"Why lower:"," More noise in short-term data",[37,823,824,827],{},[21,825,826],{},"Best for:"," Day traders, swing traders",[37,829,830,833],{},[21,831,832],{},"Require:"," Higher minimum r-value (≥0.4) for reliability",[220,835,837],{"id":836},"medium-term-patterns-10-30-days","Medium-Term Patterns (10-30 days)",[34,839,840,845,851,856],{},[37,841,842,844],{},[21,843,814],{}," 0.45 - 0.65",[37,846,847,850],{},[21,848,849],{},"Why higher:"," Noise smooths out over time",[37,852,853,855],{},[21,854,826],{}," Swing traders, position traders",[37,857,858,861],{},[21,859,860],{},"Sweet spot:"," Most reliable timeframe for correlation analysis",[220,863,865],{"id":864},"long-term-patterns-30-days","Long-Term Patterns (30+ days)",[34,867,868,873,879,884],{},[37,869,870,872],{},[21,871,814],{}," 0.40 - 0.70",[37,874,875,878],{},[21,876,877],{},"Why variable:"," Many factors influence long-term returns",[37,880,881,883],{},[21,882,826],{}," Position traders, investors",[37,885,886,888],{},[21,887,832],{}," Larger sample sizes to validate",[26,890,892],{"id":891},"practical-application-using-r-values","Practical Application: Using R-Values",[220,894,896],{"id":895},"step-1-filter-by-minimum-strength","Step 1: Filter by Minimum Strength",[18,898,899],{},"Only consider drivers with |r| ≥ 0.5 for your primary signals. Use 0.3-0.5 range as confirmation only.",[220,901,903],{"id":902},"step-2-check-sample-size","Step 2: Check Sample Size",[18,905,906],{},"Verify n ≥ 50 (preferably n ≥ 100) before trusting the correlation.",[220,908,910],{"id":909},"step-3-evaluate-direction","Step 3: Evaluate Direction",[34,912,913,919],{},[37,914,915,918],{},[21,916,917],{},"Positive r:"," Momentum or trend-following strategy",[37,920,921,924],{},[21,922,923],{},"Negative r:"," Mean reversion or contrarian strategy",[18,926,927],{},"Match the correlation direction to your trading style.",[220,929,931],{"id":930},"step-4-combine-multiple-drivers","Step 4: Combine Multiple Drivers",[18,933,934],{},"Look for 2-3 drivers with:",[34,936,937,940,943],{},[37,938,939],{},"Similar r-values (all strong)",[37,941,942],{},"Same direction (all positive or all negative)",[37,944,945],{},"Different types (insider + technical + fundamental)",[18,947,948],{},"When multiple strong correlations align, confidence increases exponentially.",[26,950,952],{"id":951},"real-world-example-evaluating-a-pattern","Real-World Example: Evaluating a Pattern",[220,954,956],{"id":955},"pattern-details","Pattern Details",[76,958,961],{"className":959,"code":960,"language":368},[366],"Driver: net_insider_buying_20d → forward_return_30d\nr = 0.68\nn = 176\np \u003C 0.001\nContribution: 32%\n",[83,962,960],{"__ignoreMap":81},[220,964,966],{"id":965},"step-by-step-evaluation","Step-by-Step Evaluation",[18,968,969],{},[21,970,971],{},"1. Check R-Value Strength",[34,973,974,977],{},[37,975,976],{},"r = 0.68 falls in \"strong positive\" range (0.5-0.7)",[37,978,979],{},"✅ Above our 0.5 threshold for high-quality patterns",[18,981,982],{},[21,983,984],{},"2. Verify Sample Size",[34,986,987,990],{},[37,988,989],{},"n = 176 is well above our 100+ target for robust samples",[37,991,992],{},"✅ Highly confident this isn't luck",[18,994,995],{},[21,996,997],{},"3. Confirm Significance",[34,999,1000,1003],{},[37,1001,1002],{},"p \u003C 0.001 means less than 0.1% chance this is random",[37,1004,1005],{},"✅ Statistically very significant",[18,1007,1008],{},[21,1009,1010],{},"4. Assess Contribution",[34,1012,1013,1016],{},[37,1014,1015],{},"32% contribution means this driver heavily influences the score",[37,1017,1018],{},"✅ Primary driver, not minor contributor",[220,1020,1022],{"id":1021},"conclusion","Conclusion",[18,1024,1025,1026,1029],{},"This is an ",[21,1027,1028],{},"elite pattern",". All factors align:",[34,1031,1032,1035,1038,1041],{},[37,1033,1034],{},"Strong correlation (0.68)",[37,1036,1037],{},"Large sample (176)",[37,1039,1040],{},"Highly significant (p \u003C 0.001)",[37,1042,1043],{},"Major contributor (32%)",[18,1045,1046,1049],{},[21,1047,1048],{},"Trading implication:"," When this driver appears in an opportunity, give it significant weight in your analysis.",[26,1051,1053],{"id":1052},"common-questions","Common Questions",[18,1055,1056],{},[21,1057,1058],{},"Q: What's the minimum r-value I should trade?",[18,1060,1061],{},"A: It depends on your strategy, but we recommend |r| ≥ 0.5 for primary signals and |r| ≥ 0.3 only as confirmation. Lower correlations are too noisy for reliable trading.",[18,1063,1064],{},[21,1065,1066],{},"Q: Can r-values change over time?",[18,1068,1069],{},"A: Absolutely. Market regimes change, patterns decay, and correlations evolve. We continuously revalidate patterns and update r-values as new data arrives.",[18,1071,1072],{},[21,1073,1074],{},"Q: Is r=0.9 always better than r=0.7?",[18,1076,1077],{},"A: Not necessarily. A pattern with r=0.9 and n=20 is less reliable than r=0.7 and n=200. Always consider sample size and statistical significance alongside r-value.",[18,1079,1080],{},[21,1081,1082],{},"Q: Why do you show negative correlations?",[18,1084,1085],{},"A: Negative correlations are just as valuable as positive ones. They identify mean reversion opportunities, contrarian setups, and fade signals. Direction matters less than strength.",[18,1087,1088],{},[21,1089,1090],{},"Q: How do I find the r-value for a specific opportunity?",[18,1092,1093],{},"A: Click into the opportunity's detail panel. The top drivers section shows the r-value, sample size, and contribution for each pattern.",[26,1095,1097],{"id":1096},"next-steps","Next Steps",[18,1099,1100,1101,1103],{},"Now that you understand correlation strength, learn why ",[548,1102,551],{"href":550}," is equally critical for evaluating pattern quality.",[18,1105,1106,1107,1109],{},"Ready for deeper statistical concepts? Explore ",[548,1108,584],{"href":583}," to understand p-values and confidence intervals.",[1111,1112,1113],"style",{},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":81,"searchDepth":95,"depth":95,"links":1115},[1116,1117,1121,1127,1132,1138,1144,1149,1155,1160,1161],{"id":28,"depth":95,"text":29},{"id":70,"depth":95,"text":71,"children":1118},[1119,1120],{"id":222,"depth":101,"text":223},{"id":308,"depth":101,"text":309},{"id":379,"depth":95,"text":380,"children":1122},[1123,1124,1125,1126],{"id":386,"depth":101,"text":387},{"id":418,"depth":101,"text":419},{"id":449,"depth":101,"text":450},{"id":480,"depth":101,"text":481},{"id":511,"depth":95,"text":512,"children":1128},[1129,1130,1131],{"id":518,"depth":101,"text":519},{"id":533,"depth":101,"text":534},{"id":554,"depth":101,"text":555},{"id":587,"depth":95,"text":588,"children":1133},[1134,1135,1136,1137],{"id":594,"depth":101,"text":595},{"id":620,"depth":101,"text":621},{"id":646,"depth":101,"text":647},{"id":672,"depth":101,"text":673},{"id":694,"depth":95,"text":695,"children":1139},[1140,1141,1142,1143],{"id":698,"depth":101,"text":699},{"id":728,"depth":101,"text":729},{"id":748,"depth":101,"text":749},{"id":782,"depth":101,"text":783},{"id":799,"depth":95,"text":800,"children":1145},[1146,1147,1148],{"id":806,"depth":101,"text":807},{"id":836,"depth":101,"text":837},{"id":864,"depth":101,"text":865},{"id":891,"depth":95,"text":892,"children":1150},[1151,1152,1153,1154],{"id":895,"depth":101,"text":896},{"id":902,"depth":101,"text":903},{"id":909,"depth":101,"text":910},{"id":930,"depth":101,"text":931},{"id":951,"depth":95,"text":952,"children":1156},[1157,1158,1159],{"id":955,"depth":101,"text":956},{"id":965,"depth":101,"text":966},{"id":1021,"depth":101,"text":1022},{"id":1052,"depth":95,"text":1053},{"id":1096,"depth":95,"text":1097},"analytics","Learn how to interpret correlation coefficients and what r-values tell you about pattern reliability","md","intermediate",{},"analytics/sample-size-importance","/docs/analytics/correlation-strength",[1170,1167,1171],"analytics/understanding-drivers","advanced/statistical-significance",{"title":5,"description":1163},"docs/analytics/correlation-strength",[1175,1176,1177,1162],"correlation","r-value","statistics","jRutujM36lvn_NJLBznKcZca-dq3NhMONfyANXi6Ws8",1785812714411]