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We built a system that detects economic cycle shifts before price confirms them. The edge isn’t in what the model predicts \u2014 it’s in the moments when it admits it has no idea.<\/p>\n
<\/p>\n
Most risk tools measure how much the market moved. But there’s a more important question almost nobody asks: is the market moving in a way the model recognizes, or is it doing something it’s never seen before?<\/strong><\/p>\n That difference is huge. When SPY, gold, and bonds start moving together in an unusual way, something is shifting in market structure. And that happens before price tells you anything.<\/p>\n That’s exactly the moment this system is built to catch.<\/p>\n “Markets don’t send warnings. But they leave footprints \u2014 in gold, in rates, in how equity reacts. If you know how to read them together, you get there first.”<\/em><\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” locked=”off” global_colors_info=”{}”]<\/p>\n The system works with three assets: SPY<\/strong> (the equity market), GLD<\/strong> (gold), and ^TNX<\/strong> (the 10-year Treasury yield). From those it builds four signals that, together, describe which phase of the cycle the market is in.<\/p>\n The SPY\/GLD ratio is probably the most powerful signal of the four. When it rises, money is flowing into stocks \u2014 expansion. When it falls, money is seeking safety in gold \u2014 something is getting complicated. Changes in the bond yield tell you whether the Fed is tightening or easing. SPY volatility tells you if the market is nervous. And daily returns give you the momentum pulse.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.12.31-PM.png”<\/a>; title_text=”PYTHON \u00b7 FEATURE ENGINEERING (YFINANCE” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n The model is intentionally simple \u2014 and that’s a strength. Instead of a black box with thousands of parameters, we use a single neuron whose logic you can explain in one sentence: it combines the four signals with weights that reflect real economic logic, and converts the result into a probability.<\/em><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/i1.jpeg”<\/a>; title_text=”i1″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n The four signals enter with economically meaningful weights. High volatility (+1.5) and rising rates (+0.7) push toward a contractionary regime. A high SPY\/GLD ratio (\u22120.9) and positive returns (\u22120.4) push toward expansion. The sigmoid node converts all of that into a probability between 0 and 1.<\/p>\n <\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.15.19-PM.png”<\/a>; title_text=”THE DIFFERENCE THAT CHANGES EVERYTHING” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n .<\/em><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n P(t) isn’t a smooth line \u2014 it oscillates constantly. That’s intentional. Markets don’t sit in one regime for months at a time; they shift day by day. What we care about is when that oscillation holds above the 0.60 threshold, or when the model starts doubting itself.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg”<\/a>; title_text=”F2″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|10px|10px|10px|10px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” custom_padding=”||0px|||” global_colors_info=”{}”]<\/p>\n The orange line is the model’s output for every day in the 2019\u20132024 period. The dotted line marks the 0.60 threshold. The two blocks where P(t) holds above that threshold correspond to the Covid crash (Q1 2020) and the Fed rate-hike cycle (mid 2022) \u2014 the two most significant regime changes of the period.<\/p>\n <\/p>\n The red dots on the price chart don’t mark drawdowns \u2014 they mark the days where \u03a3*(t) crossed the threshold. And what’s striking is that in both major events of the period, those dots appear right at the start of the correction, not after the fact.<\/strong> The model detected that something was changing before price confirmed it.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F3.jpeg”<\/a>; title_text=”F3″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|10px|10px|10px|10px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Top panel: SPY price with Risk-Off zones shaded and red dots marking high-uncertainty events directly on the price line. Bottom panel: \u03a3*(t) over time. The two spikes \u2014 Q1 2020 and mid 2022 \u2014 mark the exact moments when the market entered a regime the model had never seen before.<\/p>\n <\/p>\n When we put the Risk-On\/Risk-Off overlay directly on SPY, the picture becomes very clear. The 2019\u20132022 period was dominated by risk signals \u2014 not because price was falling the whole time, but because the internal market structure (vol, rates, relationship with gold) was constantly sending instability signals.<\/span><\/p>\n \n<\/div>\n <\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F4.jpeg”<\/a>; title_text=”F4″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Red zones show the periods when the model classified the environment as Risk-Off. The clean recovery of 2023\u20132024 lines up perfectly with the period when both P(t) and \u03a3*(t) stayed low \u2014 the model was operating on familiar ground with high conviction of expansion.<\/p>\n <\/em><\/p>\n <\/em><\/p>\n<\/div>\n The most powerful insight from this system isn’t the regime signal \u2014 it’s how that signal translates into position size. Instead of going all-in or all-out, the model continuously adjusts exposure based on its own confidence level. When it doesn’t know, it bets less. When it knows, it bets more.<\/strong><\/p>\n <\/strong><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F5.jpeg”<\/a>; title_text=”F5″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Top panel: cumulative return of the strategy (green) vs holding SPY untouched (blue). Middle panel: how position size varies over time \u2014 it contracts exactly when uncertainty is highest. Bottom panel: \u03a3*(t) in sync with the other two. Period 2019\u20132024. Strategy Sharpe: 1.86 vs 0.81. Max drawdown: \u22127.2% vs \u221232.5%.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.21.14-PM.png”<\/a>; title_text=”Screenshot 2026-04-20 at 8.21.14\u202fPM” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n These animations show how the model would process new information every two trading weeks. The most interesting part is watching the red shading and uncertainty spikes appear before<\/em> price drops \u2014 not as hindsight analysis, but as live signals.<\/p>\n<\/section>\n [\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F6.gif”<\/a>; title_text=”F6″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Each frame adds 14 trading days. Watch how the Risk-Off shading covers stress periods before they’re confirmed by the final price of each period<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F7.gif”<\/a>; title_text=”F7″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n The red peaks above the 0.65 threshold are the signal that the model has entered unknown territory. Notice how after each major transition, \u03a3*(t) drops quickly \u2014 the model re-learns the new market structure and returns to operating with confidence.<\/p>\n <\/p>\n <\/p>\n <\/p>\nWhat the model reads and why<\/h2>\n
How the model works under the hood<\/h2>\n
P(t) close to 0 \u2192 market in expansion, risk-on.<\/div>\n
\u03b1 = 0.06 \u00b7 blends realised volatility with the model’s internal disagreement<\/div>\nWhat the model sees over time<\/h2>\n
When the model doesn’t know \u2014 that’s the signal<\/h2>\n
The full cycle map over price<\/h2>\n
From model to portfolio: sizing by confidence<\/h2>\n
\u03a3*(t) = 0.33 \u2192 half the capital (50%)
\u03a3*(t) = 1.00 \u2192 a quarter of the capital (25%)<\/div>\nThe system learning in real time<\/h2>\n
<\/ul>\n<\/div>\n
The signals in practice<\/h2>\n
![https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.12.31-PM.png”<\/a>; title_text=”PYTHON \u00b7 FEATURE ENGINEERING (YFINANCE” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nHow the model works under the hood<\/h2>\nThe model is intentionally simple \u2014 and that’s a strength. Instead of a black box with thousands of parameters, we use a single neuron whose logic you can explain in one sentence: it combines the four signals with weights that reflect real economic logic, and converts the result into a probability.<\/em><\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/i1.jpeg”<\/a>; title_text=”i1″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n<\/div>\n\n\nFIGURE 1 \u00b7 NN ARCHITECTURE<\/div>\nThe four signals enter with economically meaningful weights. High volatility (+1.5) and rising rates (+0.7) push toward a contractionary regime. A high SPY\/GLD ratio (\u22120.9) and positive returns (\u22120.4) push toward expansion. The sigmoid node converts all of that into a probability between 0 and 1.<\/p>\nFORMULA \u00b7 REGIME PROBABILITY<\/div>\nz(t) = \u22120.4\u00b7ret + 1.5\u00b7vol \u2212 0.9\u00b7ratio + 0.7\u00b7\u0394rate<\/div>\nP(t) = 1 \/ (1 + e\u2212z<\/sup>)<\/div>\nP(t) close to 1 \u2192 market in contractionary mode, risk-offP(t) close to 0 \u2192 market in expansion, risk-on.<\/div>\nSo far we have a regime probability. But there’s something more important: <\/span>how confident is the model in that probability?<\/strong> That’s where Bayesian uncertainty comes in.<\/span><\/div>\n<\/div>\n<\/div>\n <\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.15.19-PM.png”<\/a>; title_text=”THE DIFFERENCE THAT CHANGES EVERYTHING” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFORMULA \u00b7 UNCERTAINTY \u03a3*(T)<\/div>\n\u03a3*(t) = rolling_std( |P(t) \u2212 smoothed_P(t)| ) + \u03b1 \u00b7 vol(t)<\/div>\nWhen \u03a3*(t) approaches 1 \u2192 the model is seeing something it’s never seen before\u03b1 = 0.06 \u00b7 blends realised volatility with the model’s internal disagreement<\/div>\n.<\/em><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat the model sees over time<\/h2>\nP(t) isn’t a smooth line \u2014 it oscillates constantly. That’s intentional. Markets don’t sit in one regime for months at a time; they shift day by day. What we care about is when that oscillation holds above the 0.60 threshold, or when the model starts doubting itself.<\/p>\n\n<\/div>\n<\/div>\n\n<\/div>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg”<\/a>; title_text=”F2″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|10px|10px|10px|10px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” custom_padding=”||0px|||” global_colors_info=”{}”]<\/p>\nFIGURE 2 \u00b7 REGIME PROBABILITY P(T|X)<\/div>\nThe orange line is the model’s output for every day in the 2019\u20132024 period. The dotted line marks the 0.60 threshold. The two blocks where P(t) holds above that threshold correspond to the Covid crash (Q1 2020) and the Fed rate-hike cycle (mid 2022) \u2014 the two most significant regime changes of the period.<\/p>\n <\/p>\nWhen the model doesn’t know \u2014 that’s the signal<\/h2>\nThe red dots on the price chart don’t mark drawdowns \u2014 they mark the days where \u03a3*(t) crossed the threshold. And what’s striking is that in both major events of the period, those dots appear right at the start of the correction, not after the fact.<\/strong> The model detected that something was changing before price confirmed it.<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F3.jpeg”<\/a>; title_text=”F3″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|10px|10px|10px|10px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nFIGURE 3 \u00b7 SPY PRICE + UNCERTAINTY \u03a3*(T)<\/div>\nTop panel: SPY price with Risk-Off zones shaded and red dots marking high-uncertainty events directly on the price line. Bottom panel: \u03a3*(t) over time. The two spikes \u2014 Q1 2020 and mid 2022 \u2014 mark the exact moments when the market entered a regime the model had never seen before.<\/p>\n <\/p>\nThe full cycle map over price<\/h2>\nWhen we put the Risk-On\/Risk-Off overlay directly on SPY, the picture becomes very clear. The 2019\u20132022 period was dominated by risk signals \u2014 not because price was falling the whole time, but because the internal market structure (vol, rates, relationship with gold) was constantly sending instability signals.<\/span><\/p>\n\n<\/div>\n <\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F4.jpeg”<\/a>; title_text=”F4″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nFIGURE 4 \u00b7 SPY ADJ CLOSE \u00b7 RISK-ON \/ RISK-OFF<\/div>\nRed zones show the periods when the model classified the environment as Risk-Off. The clean recovery of 2023\u20132024 lines up perfectly with the period when both P(t) and \u03a3*(t) stayed low \u2014 the model was operating on familiar ground with high conviction of expansion.<\/p>\n<\/em><\/p>\n<\/em><\/p>\n<\/div>\nFrom model to portfolio: sizing by confidence<\/h2>\nThe most powerful insight from this system isn’t the regime signal \u2014 it’s how that signal translates into position size. Instead of going all-in or all-out, the model continuously adjusts exposure based on its own confidence level. When it doesn’t know, it bets less. When it knows, it bets more.<\/strong><\/p>\n<\/strong><\/p>\nPOSITION SIZING \u00b7 UNCERTAINTY-SCALED<\/div>\npos_size(t) = 1 \/ (1 + 3.0 \u00d7 \u03a3*(t))<\/div>\n\u03a3*(t) = 0.00 \u2192 full position (100%)\u03a3*(t) = 0.33 \u2192 half the capital (50%)\u03a3*(t) = 1.00 \u2192 a quarter of the capital (25%)<\/div>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F5.jpeg”<\/a>; title_text=”F5″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 5 \u00b7 BACKTEST \u00b7 STRATEGY VS BUY & HOLD SPY<\/div>\nTop panel: cumulative return of the strategy (green) vs holding SPY untouched (blue). Middle panel: how position size varies over time \u2014 it contracts exactly when uncertainty is highest. Bottom panel: \u03a3*(t) in sync with the other two. Period 2019\u20132024. Strategy Sharpe: 1.86 vs 0.81. Max drawdown: \u22127.2% vs \u221232.5%.<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.21.14-PM.png”<\/a>; title_text=”Screenshot 2026-04-20 at 8.21.14\u202fPM” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nThe system learning in real time<\/h2>\nThese animations show how the model would process new information every two trading weeks. The most interesting part is watching the red shading and uncertainty spikes appear before<\/em> price drops \u2014 not as hindsight analysis, but as live signals.<\/p>\n<\/section>\n\n<\/ul>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F6.gif”<\/a>; title_text=”F6″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 6 \u00b7 ANIMATION \u00b7 SPY PRICE WITH GROWING WINDOW<\/div>\nEach frame adds 14 trading days. Watch how the Risk-Off shading covers stress periods before they’re confirmed by the final price of each period<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F7.gif”<\/a>; title_text=”F7″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 7 \u00b7 ANIMATION \u00b7 UNCERTAINTY \u03a3*(T) WITH GROWING WINDOW<\/div>\nThe red peaks above the 0.65 threshold are the signal that the model has entered unknown territory. Notice how after each major transition, \u03a3*(t) drops quickly \u2014 the model re-learns the new market structure and returns to operating with confidence.<\/p>\n <\/p>\nThe signals in practice<\/h2>\n <\/p>\n <\/p>\n\n\n\nCONDITION<\/th>\nREGIME<\/th>\nACTION<\/th>\nWHY<\/th>\n<\/tr>\n<\/thead>\n\n\nP(t) < 0.60 AND \u03a3*(t) < 0.65<\/td>\nRisk-On<\/span><\/strong><\/span><\/td>\nFull position<\/td>\nModel is convinced we’re in expansion and operating on familiar ground<\/td>\n<\/tr>\n\nP(t) > 0.60<\/td>\nRisk-Off<\/span><\/strong><\/td>\nReduce 40\u201360%<\/td>\nCross-asset signals indicate the cycle is turning contractionary<\/td>\n<\/tr>\n\n\u03a3*(t) > 0.65<\/td>\nRisk-Off<\/span><\/strong><\/span><\/td>\nReduce 40\u201360%<\/td>\nModel is in unknown territory \u2014 something is changing even if it can’t name what<\/td>\n<\/tr>\n\nP(t) > 0.60 AND \u03a3*(t) > 0.65<\/td>\nMaximum caution<\/span><\/strong><\/span><\/td>\n25% position<\/td>\nModel is both convinced of stress and confused at the same time \u2014 worst case combination<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nREADING THE SIGNALS DAY TO DAY<\/div>\n\nFirst alert:<\/strong> \u03a3*(t) rises above 0.65 but P(t) is still below 0.60. The model is detecting that something is breaking before it can say what. Cut to half position.<\/li>\nRisk-Off confirmation:<\/strong> P(t) clears 0.60 and holds there for 5 consecutive days. Not noise \u2014 the cycle is turning. Adjust size based on where \u03a3*(t) is sitting.<\/li>\nMaximum confusion zone:<\/strong> P(t) oscillates rapidly around 0.50 while \u03a3*(t) is rising. The model doesn’t know which regime is coming. Reduce gross exposure regardless of direction.<\/li>\nBack to Risk-On:<\/strong> P(t) below 0.45 for 5 days and \u03a3*(t) declining for 10. The model is finding its footing again. Scale back up gradually.<\/li>\nMaximum conviction:<\/strong> P(t) < 0.35 and \u03a3*(t) < 0.40. The model is fully comfortable with the current regime. Full position with wide stops justified by low structural uncertainty.<\/li>\n<\/ul>\nWhat this model can’t do<\/h2>\n\n\n01<\/div>\nWeights are hand-coded<\/div>\nThe model weights reflect economic logic, not statistical optimisation. A model trained on labelled historical cycles would likely perform better out-of-sample.<\/p>\n<\/div>\n\n02<\/div>\n\u03a3*(t) is an approximation<\/div>\nTrue Bayesian uncertainty requires MC-Dropout or full variational inference. This version captures the transition signal but not the exact posterior distribution.<\/p>\n<\/div>\n\n03<\/div>\nNo precise entry timing<\/div>\nThe model tells you what conditions the market is in, not exactly when a reversal will happen. A high-uncertainty regime can persist for weeks before resolving.<\/p>\n\nThe best signal a model can give you isn’t what it thinks will happen. It’s when it tells you it doesn’t know \u2014 because that’s the most honest thing it can do.<\/em><\/span><\/p>\n<\/div>\n<\/div>\n[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” parallax__hover=”off” parallax_method__hover=”on” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/gestion-patrimonial-pro-investing.png”<\/a>; title_text=”wealth management pro” url=”https:\/\/proinvesting.teachable.com\/p\/investor1″<\/a>; align_tablet=”center” align_phone=”center” align_last_edited=”on|desktop” _builder_version=”4.18.0″ border_radii=”on|5px|5px|5px|5px” border_color_all=”RGBA(255,255,255,0)” global_colors_info=”{}” border_radii__hover=”on||||” border_radii__hover_enabled=”on||||”][\/et_pb_image][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/2-1.png”<\/a>; title_text=”financial education” 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We built a system that detects economic cycle shifts before price confirms them. The edge isn’t in what the model predicts \u2014 it’s in the moments when it admits it has no idea. The problem classic models ignore Most risk tools measure how much the market moved. […]<\/p>\n","protected":false},"author":6,"featured_media":8122,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_seo_schema_type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[29],"tags":[],"class_list":["post-8109","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-quant-research"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg?fit=2000%2C571&ssl=1","jetpack-related-posts":[],"jetpack_shortlink":"https:\/\/wp.me\/pcZIdA-26N","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/comments?post=8109"}],"version-history":[{"count":18,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions"}],"predecessor-version":[{"id":8141,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions\/8141"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media\/8122"}],"wp:attachment":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media?parent=8109"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/categories?post=8109"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/tags?post=8109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}](\"https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.12.31-PM.png”\"){kind=link}
![https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/i1.jpeg”<\/a>; title_text=”i1″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n<\/div>\n\n\nFIGURE 1 \u00b7 NN ARCHITECTURE<\/div>\nThe four signals enter with economically meaningful weights. High volatility (+1.5) and rising rates (+0.7) push toward a contractionary regime. A high SPY\/GLD ratio (\u22120.9) and positive returns (\u22120.4) push toward expansion. The sigmoid node converts all of that into a probability between 0 and 1.<\/p>\nFORMULA \u00b7 REGIME PROBABILITY<\/div>\nz(t) = \u22120.4\u00b7ret + 1.5\u00b7vol \u2212 0.9\u00b7ratio + 0.7\u00b7\u0394rate<\/div>\nP(t) = 1 \/ (1 + e\u2212z<\/sup>)<\/div>\nP(t) close to 1 \u2192 market in contractionary mode, risk-offP(t) close to 0 \u2192 market in expansion, risk-on.<\/div>\nSo far we have a regime probability. But there’s something more important: <\/span>how confident is the model in that probability?<\/strong> That’s where Bayesian uncertainty comes in.<\/span><\/div>\n<\/div>\n<\/div>\n <\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.15.19-PM.png”<\/a>; title_text=”THE DIFFERENCE THAT CHANGES EVERYTHING” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFORMULA \u00b7 UNCERTAINTY \u03a3*(T)<\/div>\n\u03a3*(t) = rolling_std( |P(t) \u2212 smoothed_P(t)| ) + \u03b1 \u00b7 vol(t)<\/div>\nWhen \u03a3*(t) approaches 1 \u2192 the model is seeing something it’s never seen before\u03b1 = 0.06 \u00b7 blends realised volatility with the model’s internal disagreement<\/div>\n.<\/em><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat the model sees over time<\/h2>\nP(t) isn’t a smooth line \u2014 it oscillates constantly. That’s intentional. Markets don’t sit in one regime for months at a time; they shift day by day. What we care about is when that oscillation holds above the 0.60 threshold, or when the model starts doubting itself.<\/p>\n\n<\/div>\n<\/div>\n\n<\/div>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg”<\/a>; title_text=”F2″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|10px|10px|10px|10px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” custom_padding=”||0px|||” global_colors_info=”{}”]<\/p>\nFIGURE 2 \u00b7 REGIME PROBABILITY P(T|X)<\/div>\nThe orange line is the model’s output for every day in the 2019\u20132024 period. The dotted line marks the 0.60 threshold. The two blocks where P(t) holds above that threshold correspond to the Covid crash (Q1 2020) and the Fed rate-hike cycle (mid 2022) \u2014 the two most significant regime changes of the period.<\/p>\n <\/p>\nWhen the model doesn’t know \u2014 that’s the signal<\/h2>\nThe red dots on the price chart don’t mark drawdowns \u2014 they mark the days where \u03a3*(t) crossed the threshold. And what’s striking is that in both major events of the period, those dots appear right at the start of the correction, not after the fact.<\/strong> The model detected that something was changing before price confirmed it.<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F3.jpeg”<\/a>; title_text=”F3″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|10px|10px|10px|10px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nFIGURE 3 \u00b7 SPY PRICE + UNCERTAINTY \u03a3*(T)<\/div>\nTop panel: SPY price with Risk-Off zones shaded and red dots marking high-uncertainty events directly on the price line. Bottom panel: \u03a3*(t) over time. The two spikes \u2014 Q1 2020 and mid 2022 \u2014 mark the exact moments when the market entered a regime the model had never seen before.<\/p>\n <\/p>\nThe full cycle map over price<\/h2>\nWhen we put the Risk-On\/Risk-Off overlay directly on SPY, the picture becomes very clear. The 2019\u20132022 period was dominated by risk signals \u2014 not because price was falling the whole time, but because the internal market structure (vol, rates, relationship with gold) was constantly sending instability signals.<\/span><\/p>\n\n<\/div>\n <\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F4.jpeg”<\/a>; title_text=”F4″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nFIGURE 4 \u00b7 SPY ADJ CLOSE \u00b7 RISK-ON \/ RISK-OFF<\/div>\nRed zones show the periods when the model classified the environment as Risk-Off. The clean recovery of 2023\u20132024 lines up perfectly with the period when both P(t) and \u03a3*(t) stayed low \u2014 the model was operating on familiar ground with high conviction of expansion.<\/p>\n<\/em><\/p>\n<\/em><\/p>\n<\/div>\nFrom model to portfolio: sizing by confidence<\/h2>\nThe most powerful insight from this system isn’t the regime signal \u2014 it’s how that signal translates into position size. Instead of going all-in or all-out, the model continuously adjusts exposure based on its own confidence level. When it doesn’t know, it bets less. When it knows, it bets more.<\/strong><\/p>\n<\/strong><\/p>\nPOSITION SIZING \u00b7 UNCERTAINTY-SCALED<\/div>\npos_size(t) = 1 \/ (1 + 3.0 \u00d7 \u03a3*(t))<\/div>\n\u03a3*(t) = 0.00 \u2192 full position (100%)\u03a3*(t) = 0.33 \u2192 half the capital (50%)\u03a3*(t) = 1.00 \u2192 a quarter of the capital (25%)<\/div>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F5.jpeg”<\/a>; title_text=”F5″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 5 \u00b7 BACKTEST \u00b7 STRATEGY VS BUY & HOLD SPY<\/div>\nTop panel: cumulative return of the strategy (green) vs holding SPY untouched (blue). Middle panel: how position size varies over time \u2014 it contracts exactly when uncertainty is highest. Bottom panel: \u03a3*(t) in sync with the other two. Period 2019\u20132024. Strategy Sharpe: 1.86 vs 0.81. Max drawdown: \u22127.2% vs \u221232.5%.<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.21.14-PM.png”<\/a>; title_text=”Screenshot 2026-04-20 at 8.21.14\u202fPM” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nThe system learning in real time<\/h2>\nThese animations show how the model would process new information every two trading weeks. The most interesting part is watching the red shading and uncertainty spikes appear before<\/em> price drops \u2014 not as hindsight analysis, but as live signals.<\/p>\n<\/section>\n\n<\/ul>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F6.gif”<\/a>; title_text=”F6″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 6 \u00b7 ANIMATION \u00b7 SPY PRICE WITH GROWING WINDOW<\/div>\nEach frame adds 14 trading days. Watch how the Risk-Off shading covers stress periods before they’re confirmed by the final price of each period<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F7.gif”<\/a>; title_text=”F7″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 7 \u00b7 ANIMATION \u00b7 UNCERTAINTY \u03a3*(T) WITH GROWING WINDOW<\/div>\nThe red peaks above the 0.65 threshold are the signal that the model has entered unknown territory. Notice how after each major transition, \u03a3*(t) drops quickly \u2014 the model re-learns the new market structure and returns to operating with confidence.<\/p>\n <\/p>\nThe signals in practice<\/h2>\n <\/p>\n <\/p>\n\n\n\nCONDITION<\/th>\nREGIME<\/th>\nACTION<\/th>\nWHY<\/th>\n<\/tr>\n<\/thead>\n\n\nP(t) < 0.60 AND \u03a3*(t) < 0.65<\/td>\nRisk-On<\/span><\/strong><\/span><\/td>\nFull position<\/td>\nModel is convinced we’re in expansion and operating on familiar ground<\/td>\n<\/tr>\n\nP(t) > 0.60<\/td>\nRisk-Off<\/span><\/strong><\/td>\nReduce 40\u201360%<\/td>\nCross-asset signals indicate the cycle is turning contractionary<\/td>\n<\/tr>\n\n\u03a3*(t) > 0.65<\/td>\nRisk-Off<\/span><\/strong><\/span><\/td>\nReduce 40\u201360%<\/td>\nModel is in unknown territory \u2014 something is changing even if it can’t name what<\/td>\n<\/tr>\n\nP(t) > 0.60 AND \u03a3*(t) > 0.65<\/td>\nMaximum caution<\/span><\/strong><\/span><\/td>\n25% position<\/td>\nModel is both convinced of stress and confused at the same time \u2014 worst case combination<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nREADING THE SIGNALS DAY TO DAY<\/div>\n\nFirst alert:<\/strong> \u03a3*(t) rises above 0.65 but P(t) is still below 0.60. The model is detecting that something is breaking before it can say what. Cut to half position.<\/li>\nRisk-Off confirmation:<\/strong> P(t) clears 0.60 and holds there for 5 consecutive days. Not noise \u2014 the cycle is turning. Adjust size based on where \u03a3*(t) is sitting.<\/li>\nMaximum confusion zone:<\/strong> P(t) oscillates rapidly around 0.50 while \u03a3*(t) is rising. The model doesn’t know which regime is coming. Reduce gross exposure regardless of direction.<\/li>\nBack to Risk-On:<\/strong> P(t) below 0.45 for 5 days and \u03a3*(t) declining for 10. The model is finding its footing again. Scale back up gradually.<\/li>\nMaximum conviction:<\/strong> P(t) < 0.35 and \u03a3*(t) < 0.40. The model is fully comfortable with the current regime. Full position with wide stops justified by low structural uncertainty.<\/li>\n<\/ul>\nWhat this model can’t do<\/h2>\n\n\n01<\/div>\nWeights are hand-coded<\/div>\nThe model weights reflect economic logic, not statistical optimisation. A model trained on labelled historical cycles would likely perform better out-of-sample.<\/p>\n<\/div>\n\n02<\/div>\n\u03a3*(t) is an approximation<\/div>\nTrue Bayesian uncertainty requires MC-Dropout or full variational inference. This version captures the transition signal but not the exact posterior distribution.<\/p>\n<\/div>\n\n03<\/div>\nNo precise entry timing<\/div>\nThe model tells you what conditions the market is in, not exactly when a reversal will happen. A high-uncertainty regime can persist for weeks before resolving.<\/p>\n\nThe best signal a model can give you isn’t what it thinks will happen. It’s when it tells you it doesn’t know \u2014 because that’s the most honest thing it can do.<\/em><\/span><\/p>\n<\/div>\n<\/div>\n[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” parallax__hover=”off” parallax_method__hover=”on” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/gestion-patrimonial-pro-investing.png”<\/a>; title_text=”wealth management pro” url=”https:\/\/proinvesting.teachable.com\/p\/investor1″<\/a>; align_tablet=”center” align_phone=”center” align_last_edited=”on|desktop” _builder_version=”4.18.0″ border_radii=”on|5px|5px|5px|5px” border_color_all=”RGBA(255,255,255,0)” global_colors_info=”{}” border_radii__hover=”on||||” border_radii__hover_enabled=”on||||”][\/et_pb_image][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/2-1.png”<\/a>; title_text=”financial education” 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follow_button=”off” url_new_window=”on”]twitter[\/et_pb_social_media_follow_network][et_pb_social_media_follow_network social_network=”youtube” url=”https:\/\/www.youtube.com\/channel\/UCggDZSxWMl_utcIdEfj-8UA”<\/a>; _builder_version=”4.18.0″ background_color=”#1a0a38″ custom_margin=”|4px||4px||true” custom_padding=”8px|8px|8px|8px|true|true” global_colors_info=”{}” background__hover_enabled=”on” background_color__hover=”#00ac69″ follow_button=”off” url_new_window=”on”]youtube[\/et_pb_social_media_follow_network][\/et_pb_social_media_follow][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"When the ModelDoesn’t Know \u2014That’s the Signal. We built a system that detects economic cycle shifts before price confirms them. The edge isn’t in what the model predicts \u2014 it’s in the moments when it admits it has no idea. The problem classic models ignore Most risk tools measure how much the market moved. […]<\/p>\n","protected":false},"author":6,"featured_media":8122,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_seo_schema_type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[29],"tags":[],"class_list":["post-8109","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-quant-research"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg?fit=2000%2C571&ssl=1","jetpack-related-posts":[],"jetpack_shortlink":"https:\/\/wp.me\/pcZIdA-26N","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/comments?post=8109"}],"version-history":[{"count":18,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions"}],"predecessor-version":[{"id":8141,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions\/8141"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media\/8122"}],"wp:attachment":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media?parent=8109"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/categories?post=8109"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/tags?post=8109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}](\"https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/i1.jpeg”\"){kind=link}
![https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.15.19-PM.png”<\/a>; title_text=”THE DIFFERENCE THAT CHANGES EVERYTHING” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFORMULA \u00b7 UNCERTAINTY \u03a3*(T)<\/div>\n\u03a3*(t) = rolling_std( |P(t) \u2212 smoothed_P(t)| ) + \u03b1 \u00b7 vol(t)<\/div>\nWhen \u03a3*(t) approaches 1 \u2192 the model is seeing something it’s never seen before\u03b1 = 0.06 \u00b7 blends realised volatility with the model’s internal disagreement<\/div>\n.<\/em><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat the model sees over time<\/h2>\nP(t) isn’t a smooth line \u2014 it oscillates constantly. That’s intentional. Markets don’t sit in one regime for months at a time; they shift day by day. What we care about is when that oscillation holds above the 0.60 threshold, or when the model starts doubting itself.<\/p>\n\n<\/div>\n<\/div>\n\n<\/div>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg”<\/a>; title_text=”F2″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|10px|10px|10px|10px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” custom_padding=”||0px|||” global_colors_info=”{}”]<\/p>\nFIGURE 2 \u00b7 REGIME PROBABILITY P(T|X)<\/div>\nThe orange line is the model’s output for every day in the 2019\u20132024 period. The dotted line marks the 0.60 threshold. The two blocks where P(t) holds above that threshold correspond to the Covid crash (Q1 2020) and the Fed rate-hike cycle (mid 2022) \u2014 the two most significant regime changes of the period.<\/p>\n <\/p>\nWhen the model doesn’t know \u2014 that’s the signal<\/h2>\nThe red dots on the price chart don’t mark drawdowns \u2014 they mark the days where \u03a3*(t) crossed the threshold. And what’s striking is that in both major events of the period, those dots appear right at the start of the correction, not after the fact.<\/strong> The model detected that something was changing before price confirmed it.<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F3.jpeg”<\/a>; title_text=”F3″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|10px|10px|10px|10px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nFIGURE 3 \u00b7 SPY PRICE + UNCERTAINTY \u03a3*(T)<\/div>\nTop panel: SPY price with Risk-Off zones shaded and red dots marking high-uncertainty events directly on the price line. Bottom panel: \u03a3*(t) over time. The two spikes \u2014 Q1 2020 and mid 2022 \u2014 mark the exact moments when the market entered a regime the model had never seen before.<\/p>\n <\/p>\nThe full cycle map over price<\/h2>\nWhen we put the Risk-On\/Risk-Off overlay directly on SPY, the picture becomes very clear. The 2019\u20132022 period was dominated by risk signals \u2014 not because price was falling the whole time, but because the internal market structure (vol, rates, relationship with gold) was constantly sending instability signals.<\/span><\/p>\n\n<\/div>\n <\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F4.jpeg”<\/a>; title_text=”F4″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nFIGURE 4 \u00b7 SPY ADJ CLOSE \u00b7 RISK-ON \/ RISK-OFF<\/div>\nRed zones show the periods when the model classified the environment as Risk-Off. The clean recovery of 2023\u20132024 lines up perfectly with the period when both P(t) and \u03a3*(t) stayed low \u2014 the model was operating on familiar ground with high conviction of expansion.<\/p>\n<\/em><\/p>\n<\/em><\/p>\n<\/div>\nFrom model to portfolio: sizing by confidence<\/h2>\nThe most powerful insight from this system isn’t the regime signal \u2014 it’s how that signal translates into position size. Instead of going all-in or all-out, the model continuously adjusts exposure based on its own confidence level. When it doesn’t know, it bets less. When it knows, it bets more.<\/strong><\/p>\n<\/strong><\/p>\nPOSITION SIZING \u00b7 UNCERTAINTY-SCALED<\/div>\npos_size(t) = 1 \/ (1 + 3.0 \u00d7 \u03a3*(t))<\/div>\n\u03a3*(t) = 0.00 \u2192 full position (100%)\u03a3*(t) = 0.33 \u2192 half the capital (50%)\u03a3*(t) = 1.00 \u2192 a quarter of the capital (25%)<\/div>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F5.jpeg”<\/a>; title_text=”F5″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 5 \u00b7 BACKTEST \u00b7 STRATEGY VS BUY & HOLD SPY<\/div>\nTop panel: cumulative return of the strategy (green) vs holding SPY untouched (blue). Middle panel: how position size varies over time \u2014 it contracts exactly when uncertainty is highest. Bottom panel: \u03a3*(t) in sync with the other two. Period 2019\u20132024. Strategy Sharpe: 1.86 vs 0.81. Max drawdown: \u22127.2% vs \u221232.5%.<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.21.14-PM.png”<\/a>; title_text=”Screenshot 2026-04-20 at 8.21.14\u202fPM” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nThe system learning in real time<\/h2>\nThese animations show how the model would process new information every two trading weeks. The most interesting part is watching the red shading and uncertainty spikes appear before<\/em> price drops \u2014 not as hindsight analysis, but as live signals.<\/p>\n<\/section>\n\n<\/ul>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F6.gif”<\/a>; title_text=”F6″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 6 \u00b7 ANIMATION \u00b7 SPY PRICE WITH GROWING WINDOW<\/div>\nEach frame adds 14 trading days. Watch how the Risk-Off shading covers stress periods before they’re confirmed by the final price of each period<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F7.gif”<\/a>; title_text=”F7″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 7 \u00b7 ANIMATION \u00b7 UNCERTAINTY \u03a3*(T) WITH GROWING WINDOW<\/div>\nThe red peaks above the 0.65 threshold are the signal that the model has entered unknown territory. Notice how after each major transition, \u03a3*(t) drops quickly \u2014 the model re-learns the new market structure and returns to operating with confidence.<\/p>\n <\/p>\nThe signals in practice<\/h2>\n <\/p>\n <\/p>\n\n\n\nCONDITION<\/th>\nREGIME<\/th>\nACTION<\/th>\nWHY<\/th>\n<\/tr>\n<\/thead>\n\n\nP(t) < 0.60 AND \u03a3*(t) < 0.65<\/td>\nRisk-On<\/span><\/strong><\/span><\/td>\nFull position<\/td>\nModel is convinced we’re in expansion and operating on familiar ground<\/td>\n<\/tr>\n\nP(t) > 0.60<\/td>\nRisk-Off<\/span><\/strong><\/td>\nReduce 40\u201360%<\/td>\nCross-asset signals indicate the cycle is turning contractionary<\/td>\n<\/tr>\n\n\u03a3*(t) > 0.65<\/td>\nRisk-Off<\/span><\/strong><\/span><\/td>\nReduce 40\u201360%<\/td>\nModel is in unknown territory \u2014 something is changing even if it can’t name what<\/td>\n<\/tr>\n\nP(t) > 0.60 AND \u03a3*(t) > 0.65<\/td>\nMaximum caution<\/span><\/strong><\/span><\/td>\n25% position<\/td>\nModel is both convinced of stress and confused at the same time \u2014 worst case combination<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nREADING THE SIGNALS DAY TO DAY<\/div>\n\nFirst alert:<\/strong> \u03a3*(t) rises above 0.65 but P(t) is still below 0.60. The model is detecting that something is breaking before it can say what. Cut to half position.<\/li>\nRisk-Off confirmation:<\/strong> P(t) clears 0.60 and holds there for 5 consecutive days. Not noise \u2014 the cycle is turning. Adjust size based on where \u03a3*(t) is sitting.<\/li>\nMaximum confusion zone:<\/strong> P(t) oscillates rapidly around 0.50 while \u03a3*(t) is rising. The model doesn’t know which regime is coming. Reduce gross exposure regardless of direction.<\/li>\nBack to Risk-On:<\/strong> P(t) below 0.45 for 5 days and \u03a3*(t) declining for 10. The model is finding its footing again. Scale back up gradually.<\/li>\nMaximum conviction:<\/strong> P(t) < 0.35 and \u03a3*(t) < 0.40. The model is fully comfortable with the current regime. Full position with wide stops justified by low structural uncertainty.<\/li>\n<\/ul>\nWhat this model can’t do<\/h2>\n\n\n01<\/div>\nWeights are hand-coded<\/div>\nThe model weights reflect economic logic, not statistical optimisation. A model trained on labelled historical cycles would likely perform better out-of-sample.<\/p>\n<\/div>\n\n02<\/div>\n\u03a3*(t) is an approximation<\/div>\nTrue Bayesian uncertainty requires MC-Dropout or full variational inference. This version captures the transition signal but not the exact posterior distribution.<\/p>\n<\/div>\n\n03<\/div>\nNo precise entry timing<\/div>\nThe model tells you what conditions the market is in, not exactly when a reversal will happen. A high-uncertainty regime can persist for weeks before resolving.<\/p>\n\nThe best signal a model can give you isn’t what it thinks will happen. It’s when it tells you it doesn’t know \u2014 because that’s the most honest thing it can do.<\/em><\/span><\/p>\n<\/div>\n<\/div>\n[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” parallax__hover=”off” parallax_method__hover=”on” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/gestion-patrimonial-pro-investing.png”<\/a>; title_text=”wealth management pro” url=”https:\/\/proinvesting.teachable.com\/p\/investor1″<\/a>; align_tablet=”center” align_phone=”center” align_last_edited=”on|desktop” _builder_version=”4.18.0″ border_radii=”on|5px|5px|5px|5px” border_color_all=”RGBA(255,255,255,0)” global_colors_info=”{}” border_radii__hover=”on||||” border_radii__hover_enabled=”on||||”][\/et_pb_image][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/2-1.png”<\/a>; title_text=”financial education” 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We built a system that detects economic cycle shifts before price confirms them. The edge isn’t in what the model predicts \u2014 it’s in the moments when it admits it has no idea. The problem classic models ignore Most risk tools measure how much the market moved. 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![https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg”<\/a>; title_text=”F2″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|10px|10px|10px|10px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” custom_padding=”||0px|||” global_colors_info=”{}”]<\/p>\nFIGURE 2 \u00b7 REGIME PROBABILITY P(T|X)<\/div>\nThe orange line is the model’s output for every day in the 2019\u20132024 period. The dotted line marks the 0.60 threshold. The two blocks where P(t) holds above that threshold correspond to the Covid crash (Q1 2020) and the Fed rate-hike cycle (mid 2022) \u2014 the two most significant regime changes of the period.<\/p>\n <\/p>\nWhen the model doesn’t know \u2014 that’s the signal<\/h2>\nThe red dots on the price chart don’t mark drawdowns \u2014 they mark the days where \u03a3*(t) crossed the threshold. And what’s striking is that in both major events of the period, those dots appear right at the start of the correction, not after the fact.<\/strong> The model detected that something was changing before price confirmed it.<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F3.jpeg”<\/a>; title_text=”F3″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|10px|10px|10px|10px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nFIGURE 3 \u00b7 SPY PRICE + UNCERTAINTY \u03a3*(T)<\/div>\nTop panel: SPY price with Risk-Off zones shaded and red dots marking high-uncertainty events directly on the price line. Bottom panel: \u03a3*(t) over time. The two spikes \u2014 Q1 2020 and mid 2022 \u2014 mark the exact moments when the market entered a regime the model had never seen before.<\/p>\n <\/p>\nThe full cycle map over price<\/h2>\nWhen we put the Risk-On\/Risk-Off overlay directly on SPY, the picture becomes very clear. The 2019\u20132022 period was dominated by risk signals \u2014 not because price was falling the whole time, but because the internal market structure (vol, rates, relationship with gold) was constantly sending instability signals.<\/span><\/p>\n\n<\/div>\n <\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F4.jpeg”<\/a>; title_text=”F4″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nFIGURE 4 \u00b7 SPY ADJ CLOSE \u00b7 RISK-ON \/ RISK-OFF<\/div>\nRed zones show the periods when the model classified the environment as Risk-Off. The clean recovery of 2023\u20132024 lines up perfectly with the period when both P(t) and \u03a3*(t) stayed low \u2014 the model was operating on familiar ground with high conviction of expansion.<\/p>\n<\/em><\/p>\n<\/em><\/p>\n<\/div>\nFrom model to portfolio: sizing by confidence<\/h2>\nThe most powerful insight from this system isn’t the regime signal \u2014 it’s how that signal translates into position size. Instead of going all-in or all-out, the model continuously adjusts exposure based on its own confidence level. When it doesn’t know, it bets less. When it knows, it bets more.<\/strong><\/p>\n<\/strong><\/p>\nPOSITION SIZING \u00b7 UNCERTAINTY-SCALED<\/div>\npos_size(t) = 1 \/ (1 + 3.0 \u00d7 \u03a3*(t))<\/div>\n\u03a3*(t) = 0.00 \u2192 full position (100%)\u03a3*(t) = 0.33 \u2192 half the capital (50%)\u03a3*(t) = 1.00 \u2192 a quarter of the capital (25%)<\/div>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F5.jpeg”<\/a>; title_text=”F5″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 5 \u00b7 BACKTEST \u00b7 STRATEGY VS BUY & HOLD SPY<\/div>\nTop panel: cumulative return of the strategy (green) vs holding SPY untouched (blue). Middle panel: how position size varies over time \u2014 it contracts exactly when uncertainty is highest. Bottom panel: \u03a3*(t) in sync with the other two. Period 2019\u20132024. Strategy Sharpe: 1.86 vs 0.81. Max drawdown: \u22127.2% vs \u221232.5%.<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.21.14-PM.png”<\/a>; title_text=”Screenshot 2026-04-20 at 8.21.14\u202fPM” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nThe system learning in real time<\/h2>\nThese animations show how the model would process new information every two trading weeks. The most interesting part is watching the red shading and uncertainty spikes appear before<\/em> price drops \u2014 not as hindsight analysis, but as live signals.<\/p>\n<\/section>\n\n<\/ul>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F6.gif”<\/a>; title_text=”F6″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 6 \u00b7 ANIMATION \u00b7 SPY PRICE WITH GROWING WINDOW<\/div>\nEach frame adds 14 trading days. Watch how the Risk-Off shading covers stress periods before they’re confirmed by the final price of each period<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F7.gif”<\/a>; title_text=”F7″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 7 \u00b7 ANIMATION \u00b7 UNCERTAINTY \u03a3*(T) WITH GROWING WINDOW<\/div>\nThe red peaks above the 0.65 threshold are the signal that the model has entered unknown territory. Notice how after each major transition, \u03a3*(t) drops quickly \u2014 the model re-learns the new market structure and returns to operating with confidence.<\/p>\n <\/p>\nThe signals in practice<\/h2>\n <\/p>\n <\/p>\n\n\n\nCONDITION<\/th>\nREGIME<\/th>\nACTION<\/th>\nWHY<\/th>\n<\/tr>\n<\/thead>\n\n\nP(t) < 0.60 AND \u03a3*(t) < 0.65<\/td>\nRisk-On<\/span><\/strong><\/span><\/td>\nFull position<\/td>\nModel is convinced we’re in expansion and operating on familiar ground<\/td>\n<\/tr>\n\nP(t) > 0.60<\/td>\nRisk-Off<\/span><\/strong><\/td>\nReduce 40\u201360%<\/td>\nCross-asset signals indicate the cycle is turning contractionary<\/td>\n<\/tr>\n\n\u03a3*(t) > 0.65<\/td>\nRisk-Off<\/span><\/strong><\/span><\/td>\nReduce 40\u201360%<\/td>\nModel is in unknown territory \u2014 something is changing even if it can’t name what<\/td>\n<\/tr>\n\nP(t) > 0.60 AND \u03a3*(t) > 0.65<\/td>\nMaximum caution<\/span><\/strong><\/span><\/td>\n25% position<\/td>\nModel is both convinced of stress and confused at the same time \u2014 worst case combination<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nREADING THE SIGNALS DAY TO DAY<\/div>\n\nFirst alert:<\/strong> \u03a3*(t) rises above 0.65 but P(t) is still below 0.60. The model is detecting that something is breaking before it can say what. Cut to half position.<\/li>\nRisk-Off confirmation:<\/strong> P(t) clears 0.60 and holds there for 5 consecutive days. Not noise \u2014 the cycle is turning. Adjust size based on where \u03a3*(t) is sitting.<\/li>\nMaximum confusion zone:<\/strong> P(t) oscillates rapidly around 0.50 while \u03a3*(t) is rising. The model doesn’t know which regime is coming. Reduce gross exposure regardless of direction.<\/li>\nBack to Risk-On:<\/strong> P(t) below 0.45 for 5 days and \u03a3*(t) declining for 10. The model is finding its footing again. Scale back up gradually.<\/li>\nMaximum conviction:<\/strong> P(t) < 0.35 and \u03a3*(t) < 0.40. The model is fully comfortable with the current regime. Full position with wide stops justified by low structural uncertainty.<\/li>\n<\/ul>\nWhat this model can’t do<\/h2>\n\n\n01<\/div>\nWeights are hand-coded<\/div>\nThe model weights reflect economic logic, not statistical optimisation. A model trained on labelled historical cycles would likely perform better out-of-sample.<\/p>\n<\/div>\n\n02<\/div>\n\u03a3*(t) is an approximation<\/div>\nTrue Bayesian uncertainty requires MC-Dropout or full variational inference. This version captures the transition signal but not the exact posterior distribution.<\/p>\n<\/div>\n\n03<\/div>\nNo precise entry timing<\/div>\nThe model tells you what conditions the market is in, not exactly when a reversal will happen. A high-uncertainty regime can persist for weeks before resolving.<\/p>\n\nThe best signal a model can give you isn’t what it thinks will happen. It’s when it tells you it doesn’t know \u2014 because that’s the most honest thing it can do.<\/em><\/span><\/p>\n<\/div>\n<\/div>\n[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” parallax__hover=”off” parallax_method__hover=”on” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/gestion-patrimonial-pro-investing.png”<\/a>; title_text=”wealth management pro” url=”https:\/\/proinvesting.teachable.com\/p\/investor1″<\/a>; align_tablet=”center” align_phone=”center” align_last_edited=”on|desktop” _builder_version=”4.18.0″ border_radii=”on|5px|5px|5px|5px” border_color_all=”RGBA(255,255,255,0)” global_colors_info=”{}” border_radii__hover=”on||||” border_radii__hover_enabled=”on||||”][\/et_pb_image][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/2-1.png”<\/a>; title_text=”financial education” 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We built a system that detects economic cycle shifts before price confirms them. The edge isn’t in what the model predicts \u2014 it’s in the moments when it admits it has no idea. The problem classic models ignore Most risk tools measure how much the market moved. […]<\/p>\n","protected":false},"author":6,"featured_media":8122,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_seo_schema_type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[29],"tags":[],"class_list":["post-8109","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-quant-research"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg?fit=2000%2C571&ssl=1","jetpack-related-posts":[],"jetpack_shortlink":"https:\/\/wp.me\/pcZIdA-26N","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/comments?post=8109"}],"version-history":[{"count":18,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions"}],"predecessor-version":[{"id":8141,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions\/8141"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media\/8122"}],"wp:attachment":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media?parent=8109"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/categories?post=8109"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/tags?post=8109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}](\"https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg”\"){kind=link}
![https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F3.jpeg”<\/a>; title_text=”F3″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|10px|10px|10px|10px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nFIGURE 3 \u00b7 SPY PRICE + UNCERTAINTY \u03a3*(T)<\/div>\nTop panel: SPY price with Risk-Off zones shaded and red dots marking high-uncertainty events directly on the price line. Bottom panel: \u03a3*(t) over time. The two spikes \u2014 Q1 2020 and mid 2022 \u2014 mark the exact moments when the market entered a regime the model had never seen before.<\/p>\n <\/p>\nThe full cycle map over price<\/h2>\nWhen we put the Risk-On\/Risk-Off overlay directly on SPY, the picture becomes very clear. The 2019\u20132022 period was dominated by risk signals \u2014 not because price was falling the whole time, but because the internal market structure (vol, rates, relationship with gold) was constantly sending instability signals.<\/span><\/p>\n\n<\/div>\n <\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F4.jpeg”<\/a>; title_text=”F4″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nFIGURE 4 \u00b7 SPY ADJ CLOSE \u00b7 RISK-ON \/ RISK-OFF<\/div>\nRed zones show the periods when the model classified the environment as Risk-Off. The clean recovery of 2023\u20132024 lines up perfectly with the period when both P(t) and \u03a3*(t) stayed low \u2014 the model was operating on familiar ground with high conviction of expansion.<\/p>\n<\/em><\/p>\n<\/em><\/p>\n<\/div>\nFrom model to portfolio: sizing by confidence<\/h2>\nThe most powerful insight from this system isn’t the regime signal \u2014 it’s how that signal translates into position size. Instead of going all-in or all-out, the model continuously adjusts exposure based on its own confidence level. When it doesn’t know, it bets less. When it knows, it bets more.<\/strong><\/p>\n<\/strong><\/p>\nPOSITION SIZING \u00b7 UNCERTAINTY-SCALED<\/div>\npos_size(t) = 1 \/ (1 + 3.0 \u00d7 \u03a3*(t))<\/div>\n\u03a3*(t) = 0.00 \u2192 full position (100%)\u03a3*(t) = 0.33 \u2192 half the capital (50%)\u03a3*(t) = 1.00 \u2192 a quarter of the capital (25%)<\/div>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F5.jpeg”<\/a>; title_text=”F5″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 5 \u00b7 BACKTEST \u00b7 STRATEGY VS BUY & HOLD SPY<\/div>\nTop panel: cumulative return of the strategy (green) vs holding SPY untouched (blue). Middle panel: how position size varies over time \u2014 it contracts exactly when uncertainty is highest. Bottom panel: \u03a3*(t) in sync with the other two. Period 2019\u20132024. Strategy Sharpe: 1.86 vs 0.81. Max drawdown: \u22127.2% vs \u221232.5%.<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.21.14-PM.png”<\/a>; title_text=”Screenshot 2026-04-20 at 8.21.14\u202fPM” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nThe system learning in real time<\/h2>\nThese animations show how the model would process new information every two trading weeks. The most interesting part is watching the red shading and uncertainty spikes appear before<\/em> price drops \u2014 not as hindsight analysis, but as live signals.<\/p>\n<\/section>\n\n<\/ul>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F6.gif”<\/a>; title_text=”F6″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 6 \u00b7 ANIMATION \u00b7 SPY PRICE WITH GROWING WINDOW<\/div>\nEach frame adds 14 trading days. Watch how the Risk-Off shading covers stress periods before they’re confirmed by the final price of each period<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F7.gif”<\/a>; title_text=”F7″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 7 \u00b7 ANIMATION \u00b7 UNCERTAINTY \u03a3*(T) WITH GROWING WINDOW<\/div>\nThe red peaks above the 0.65 threshold are the signal that the model has entered unknown territory. Notice how after each major transition, \u03a3*(t) drops quickly \u2014 the model re-learns the new market structure and returns to operating with confidence.<\/p>\n <\/p>\nThe signals in practice<\/h2>\n <\/p>\n <\/p>\n\n\n\nCONDITION<\/th>\nREGIME<\/th>\nACTION<\/th>\nWHY<\/th>\n<\/tr>\n<\/thead>\n\n\nP(t) < 0.60 AND \u03a3*(t) < 0.65<\/td>\nRisk-On<\/span><\/strong><\/span><\/td>\nFull position<\/td>\nModel is convinced we’re in expansion and operating on familiar ground<\/td>\n<\/tr>\n\nP(t) > 0.60<\/td>\nRisk-Off<\/span><\/strong><\/td>\nReduce 40\u201360%<\/td>\nCross-asset signals indicate the cycle is turning contractionary<\/td>\n<\/tr>\n\n\u03a3*(t) > 0.65<\/td>\nRisk-Off<\/span><\/strong><\/span><\/td>\nReduce 40\u201360%<\/td>\nModel is in unknown territory \u2014 something is changing even if it can’t name what<\/td>\n<\/tr>\n\nP(t) > 0.60 AND \u03a3*(t) > 0.65<\/td>\nMaximum caution<\/span><\/strong><\/span><\/td>\n25% position<\/td>\nModel is both convinced of stress and confused at the same time \u2014 worst case combination<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nREADING THE SIGNALS DAY TO DAY<\/div>\n\nFirst alert:<\/strong> \u03a3*(t) rises above 0.65 but P(t) is still below 0.60. The model is detecting that something is breaking before it can say what. Cut to half position.<\/li>\nRisk-Off confirmation:<\/strong> P(t) clears 0.60 and holds there for 5 consecutive days. Not noise \u2014 the cycle is turning. Adjust size based on where \u03a3*(t) is sitting.<\/li>\nMaximum confusion zone:<\/strong> P(t) oscillates rapidly around 0.50 while \u03a3*(t) is rising. The model doesn’t know which regime is coming. Reduce gross exposure regardless of direction.<\/li>\nBack to Risk-On:<\/strong> P(t) below 0.45 for 5 days and \u03a3*(t) declining for 10. The model is finding its footing again. Scale back up gradually.<\/li>\nMaximum conviction:<\/strong> P(t) < 0.35 and \u03a3*(t) < 0.40. The model is fully comfortable with the current regime. Full position with wide stops justified by low structural uncertainty.<\/li>\n<\/ul>\nWhat this model can’t do<\/h2>\n\n\n01<\/div>\nWeights are hand-coded<\/div>\nThe model weights reflect economic logic, not statistical optimisation. A model trained on labelled historical cycles would likely perform better out-of-sample.<\/p>\n<\/div>\n\n02<\/div>\n\u03a3*(t) is an approximation<\/div>\nTrue Bayesian uncertainty requires MC-Dropout or full variational inference. This version captures the transition signal but not the exact posterior distribution.<\/p>\n<\/div>\n\n03<\/div>\nNo precise entry timing<\/div>\nThe model tells you what conditions the market is in, not exactly when a reversal will happen. A high-uncertainty regime can persist for weeks before resolving.<\/p>\n\nThe best signal a model can give you isn’t what it thinks will happen. 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We built a system that detects economic cycle shifts before price confirms them. The edge isn’t in what the model predicts \u2014 it’s in the moments when it admits it has no idea. The problem classic models ignore Most risk tools measure how much the market moved. […]<\/p>\n","protected":false},"author":6,"featured_media":8122,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_seo_schema_type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[29],"tags":[],"class_list":["post-8109","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-quant-research"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg?fit=2000%2C571&ssl=1","jetpack-related-posts":[],"jetpack_shortlink":"https:\/\/wp.me\/pcZIdA-26N","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/comments?post=8109"}],"version-history":[{"count":18,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions"}],"predecessor-version":[{"id":8141,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions\/8141"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media\/8122"}],"wp:attachment":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media?parent=8109"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/categories?post=8109"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/tags?post=8109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}](\"https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F3.jpeg”\"){kind=link}
![https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F4.jpeg”<\/a>; title_text=”F4″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nFIGURE 4 \u00b7 SPY ADJ CLOSE \u00b7 RISK-ON \/ RISK-OFF<\/div>\nRed zones show the periods when the model classified the environment as Risk-Off. The clean recovery of 2023\u20132024 lines up perfectly with the period when both P(t) and \u03a3*(t) stayed low \u2014 the model was operating on familiar ground with high conviction of expansion.<\/p>\n<\/em><\/p>\n<\/em><\/p>\n<\/div>\nFrom model to portfolio: sizing by confidence<\/h2>\nThe most powerful insight from this system isn’t the regime signal \u2014 it’s how that signal translates into position size. Instead of going all-in or all-out, the model continuously adjusts exposure based on its own confidence level. When it doesn’t know, it bets less. When it knows, it bets more.<\/strong><\/p>\n<\/strong><\/p>\nPOSITION SIZING \u00b7 UNCERTAINTY-SCALED<\/div>\npos_size(t) = 1 \/ (1 + 3.0 \u00d7 \u03a3*(t))<\/div>\n\u03a3*(t) = 0.00 \u2192 full position (100%)\u03a3*(t) = 0.33 \u2192 half the capital (50%)\u03a3*(t) = 1.00 \u2192 a quarter of the capital (25%)<\/div>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F5.jpeg”<\/a>; title_text=”F5″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 5 \u00b7 BACKTEST \u00b7 STRATEGY VS BUY & HOLD SPY<\/div>\nTop panel: cumulative return of the strategy (green) vs holding SPY untouched (blue). Middle panel: how position size varies over time \u2014 it contracts exactly when uncertainty is highest. Bottom panel: \u03a3*(t) in sync with the other two. Period 2019\u20132024. Strategy Sharpe: 1.86 vs 0.81. Max drawdown: \u22127.2% vs \u221232.5%.<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.21.14-PM.png”<\/a>; title_text=”Screenshot 2026-04-20 at 8.21.14\u202fPM” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nThe system learning in real time<\/h2>\nThese animations show how the model would process new information every two trading weeks. The most interesting part is watching the red shading and uncertainty spikes appear before<\/em> price drops \u2014 not as hindsight analysis, but as live signals.<\/p>\n<\/section>\n\n<\/ul>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F6.gif”<\/a>; title_text=”F6″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 6 \u00b7 ANIMATION \u00b7 SPY PRICE WITH GROWING WINDOW<\/div>\nEach frame adds 14 trading days. Watch how the Risk-Off shading covers stress periods before they’re confirmed by the final price of each period<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F7.gif”<\/a>; title_text=”F7″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 7 \u00b7 ANIMATION \u00b7 UNCERTAINTY \u03a3*(T) WITH GROWING WINDOW<\/div>\nThe red peaks above the 0.65 threshold are the signal that the model has entered unknown territory. Notice how after each major transition, \u03a3*(t) drops quickly \u2014 the model re-learns the new market structure and returns to operating with confidence.<\/p>\n <\/p>\nThe signals in practice<\/h2>\n <\/p>\n <\/p>\n\n\n\nCONDITION<\/th>\nREGIME<\/th>\nACTION<\/th>\nWHY<\/th>\n<\/tr>\n<\/thead>\n\n\nP(t) < 0.60 AND \u03a3*(t) < 0.65<\/td>\nRisk-On<\/span><\/strong><\/span><\/td>\nFull position<\/td>\nModel is convinced we’re in expansion and operating on familiar ground<\/td>\n<\/tr>\n\nP(t) > 0.60<\/td>\nRisk-Off<\/span><\/strong><\/td>\nReduce 40\u201360%<\/td>\nCross-asset signals indicate the cycle is turning contractionary<\/td>\n<\/tr>\n\n\u03a3*(t) > 0.65<\/td>\nRisk-Off<\/span><\/strong><\/span><\/td>\nReduce 40\u201360%<\/td>\nModel is in unknown territory \u2014 something is changing even if it can’t name what<\/td>\n<\/tr>\n\nP(t) > 0.60 AND \u03a3*(t) > 0.65<\/td>\nMaximum caution<\/span><\/strong><\/span><\/td>\n25% position<\/td>\nModel is both convinced of stress and confused at the same time \u2014 worst case combination<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nREADING THE SIGNALS DAY TO DAY<\/div>\n\nFirst alert:<\/strong> \u03a3*(t) rises above 0.65 but P(t) is still below 0.60. The model is detecting that something is breaking before it can say what. Cut to half position.<\/li>\nRisk-Off confirmation:<\/strong> P(t) clears 0.60 and holds there for 5 consecutive days. Not noise \u2014 the cycle is turning. Adjust size based on where \u03a3*(t) is sitting.<\/li>\nMaximum confusion zone:<\/strong> P(t) oscillates rapidly around 0.50 while \u03a3*(t) is rising. The model doesn’t know which regime is coming. Reduce gross exposure regardless of direction.<\/li>\nBack to Risk-On:<\/strong> P(t) below 0.45 for 5 days and \u03a3*(t) declining for 10. The model is finding its footing again. Scale back up gradually.<\/li>\nMaximum conviction:<\/strong> P(t) < 0.35 and \u03a3*(t) < 0.40. The model is fully comfortable with the current regime. Full position with wide stops justified by low structural uncertainty.<\/li>\n<\/ul>\nWhat this model can’t do<\/h2>\n\n\n01<\/div>\nWeights are hand-coded<\/div>\nThe model weights reflect economic logic, not statistical optimisation. A model trained on labelled historical cycles would likely perform better out-of-sample.<\/p>\n<\/div>\n\n02<\/div>\n\u03a3*(t) is an approximation<\/div>\nTrue Bayesian uncertainty requires MC-Dropout or full variational inference. This version captures the transition signal but not the exact posterior distribution.<\/p>\n<\/div>\n\n03<\/div>\nNo precise entry timing<\/div>\nThe model tells you what conditions the market is in, not exactly when a reversal will happen. A high-uncertainty regime can persist for weeks before resolving.<\/p>\n\nThe best signal a model can give you isn’t what it thinks will happen. It’s when it tells you it doesn’t know \u2014 because that’s the most honest thing it can do.<\/em><\/span><\/p>\n<\/div>\n<\/div>\n[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” parallax__hover=”off” parallax_method__hover=”on” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/gestion-patrimonial-pro-investing.png”<\/a>; title_text=”wealth management pro” url=”https:\/\/proinvesting.teachable.com\/p\/investor1″<\/a>; align_tablet=”center” align_phone=”center” align_last_edited=”on|desktop” _builder_version=”4.18.0″ border_radii=”on|5px|5px|5px|5px” border_color_all=”RGBA(255,255,255,0)” global_colors_info=”{}” border_radii__hover=”on||||” border_radii__hover_enabled=”on||||”][\/et_pb_image][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/2-1.png”<\/a>; title_text=”financial education” 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We built a system that detects economic cycle shifts before price confirms them. The edge isn’t in what the model predicts \u2014 it’s in the moments when it admits it has no idea. The problem classic models ignore Most risk tools measure how much the market moved. […]<\/p>\n","protected":false},"author":6,"featured_media":8122,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_seo_schema_type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[29],"tags":[],"class_list":["post-8109","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-quant-research"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg?fit=2000%2C571&ssl=1","jetpack-related-posts":[],"jetpack_shortlink":"https:\/\/wp.me\/pcZIdA-26N","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/comments?post=8109"}],"version-history":[{"count":18,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions"}],"predecessor-version":[{"id":8141,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions\/8141"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media\/8122"}],"wp:attachment":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media?parent=8109"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/categories?post=8109"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/tags?post=8109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}](\"https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F4.jpeg”\"){kind=link}
![https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F5.jpeg”<\/a>; title_text=”F5″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 5 \u00b7 BACKTEST \u00b7 STRATEGY VS BUY & HOLD SPY<\/div>\nTop panel: cumulative return of the strategy (green) vs holding SPY untouched (blue). Middle panel: how position size varies over time \u2014 it contracts exactly when uncertainty is highest. Bottom panel: \u03a3*(t) in sync with the other two. Period 2019\u20132024. Strategy Sharpe: 1.86 vs 0.81. Max drawdown: \u22127.2% vs \u221232.5%.<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.21.14-PM.png”<\/a>; title_text=”Screenshot 2026-04-20 at 8.21.14\u202fPM” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nThe system learning in real time<\/h2>\nThese animations show how the model would process new information every two trading weeks. The most interesting part is watching the red shading and uncertainty spikes appear before<\/em> price drops \u2014 not as hindsight analysis, but as live signals.<\/p>\n<\/section>\n\n<\/ul>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F6.gif”<\/a>; title_text=”F6″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 6 \u00b7 ANIMATION \u00b7 SPY PRICE WITH GROWING WINDOW<\/div>\nEach frame adds 14 trading days. Watch how the Risk-Off shading covers stress periods before they’re confirmed by the final price of each period<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F7.gif”<\/a>; title_text=”F7″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 7 \u00b7 ANIMATION \u00b7 UNCERTAINTY \u03a3*(T) WITH GROWING WINDOW<\/div>\nThe red peaks above the 0.65 threshold are the signal that the model has entered unknown territory. Notice how after each major transition, \u03a3*(t) drops quickly \u2014 the model re-learns the new market structure and returns to operating with confidence.<\/p>\n <\/p>\nThe signals in practice<\/h2>\n <\/p>\n <\/p>\n\n\n\nCONDITION<\/th>\nREGIME<\/th>\nACTION<\/th>\nWHY<\/th>\n<\/tr>\n<\/thead>\n\n\nP(t) < 0.60 AND \u03a3*(t) < 0.65<\/td>\nRisk-On<\/span><\/strong><\/span><\/td>\nFull position<\/td>\nModel is convinced we’re in expansion and operating on familiar ground<\/td>\n<\/tr>\n\nP(t) > 0.60<\/td>\nRisk-Off<\/span><\/strong><\/td>\nReduce 40\u201360%<\/td>\nCross-asset signals indicate the cycle is turning contractionary<\/td>\n<\/tr>\n\n\u03a3*(t) > 0.65<\/td>\nRisk-Off<\/span><\/strong><\/span><\/td>\nReduce 40\u201360%<\/td>\nModel is in unknown territory \u2014 something is changing even if it can’t name what<\/td>\n<\/tr>\n\nP(t) > 0.60 AND \u03a3*(t) > 0.65<\/td>\nMaximum caution<\/span><\/strong><\/span><\/td>\n25% position<\/td>\nModel is both convinced of stress and confused at the same time \u2014 worst case combination<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nREADING THE SIGNALS DAY TO DAY<\/div>\n\nFirst alert:<\/strong> \u03a3*(t) rises above 0.65 but P(t) is still below 0.60. The model is detecting that something is breaking before it can say what. Cut to half position.<\/li>\nRisk-Off confirmation:<\/strong> P(t) clears 0.60 and holds there for 5 consecutive days. Not noise \u2014 the cycle is turning. Adjust size based on where \u03a3*(t) is sitting.<\/li>\nMaximum confusion zone:<\/strong> P(t) oscillates rapidly around 0.50 while \u03a3*(t) is rising. The model doesn’t know which regime is coming. Reduce gross exposure regardless of direction.<\/li>\nBack to Risk-On:<\/strong> P(t) below 0.45 for 5 days and \u03a3*(t) declining for 10. The model is finding its footing again. Scale back up gradually.<\/li>\nMaximum conviction:<\/strong> P(t) < 0.35 and \u03a3*(t) < 0.40. The model is fully comfortable with the current regime. Full position with wide stops justified by low structural uncertainty.<\/li>\n<\/ul>\nWhat this model can’t do<\/h2>\n\n\n01<\/div>\nWeights are hand-coded<\/div>\nThe model weights reflect economic logic, not statistical optimisation. A model trained on labelled historical cycles would likely perform better out-of-sample.<\/p>\n<\/div>\n\n02<\/div>\n\u03a3*(t) is an approximation<\/div>\nTrue Bayesian uncertainty requires MC-Dropout or full variational inference. This version captures the transition signal but not the exact posterior distribution.<\/p>\n<\/div>\n\n03<\/div>\nNo precise entry timing<\/div>\nThe model tells you what conditions the market is in, not exactly when a reversal will happen. A high-uncertainty regime can persist for weeks before resolving.<\/p>\n\nThe best signal a model can give you isn’t what it thinks will happen. It’s when it tells you it doesn’t know \u2014 because that’s the most honest thing it can do.<\/em><\/span><\/p>\n<\/div>\n<\/div>\n[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” parallax__hover=”off” parallax_method__hover=”on” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/gestion-patrimonial-pro-investing.png”<\/a>; title_text=”wealth management pro” url=”https:\/\/proinvesting.teachable.com\/p\/investor1″<\/a>; align_tablet=”center” align_phone=”center” align_last_edited=”on|desktop” _builder_version=”4.18.0″ border_radii=”on|5px|5px|5px|5px” border_color_all=”RGBA(255,255,255,0)” global_colors_info=”{}” border_radii__hover=”on||||” border_radii__hover_enabled=”on||||”][\/et_pb_image][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/2-1.png”<\/a>; title_text=”financial education” 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We built a system that detects economic cycle shifts before price confirms them. The edge isn’t in what the model predicts \u2014 it’s in the moments when it admits it has no idea. The problem classic models ignore Most risk tools measure how much the market moved. […]<\/p>\n","protected":false},"author":6,"featured_media":8122,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_seo_schema_type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[29],"tags":[],"class_list":["post-8109","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-quant-research"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg?fit=2000%2C571&ssl=1","jetpack-related-posts":[],"jetpack_shortlink":"https:\/\/wp.me\/pcZIdA-26N","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/comments?post=8109"}],"version-history":[{"count":18,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions"}],"predecessor-version":[{"id":8141,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions\/8141"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media\/8122"}],"wp:attachment":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media?parent=8109"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/categories?post=8109"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/tags?post=8109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}](\"https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F5.jpeg”\"){kind=link}
![https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.21.14-PM.png”<\/a>; title_text=”Screenshot 2026-04-20 at 8.21.14\u202fPM” _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\nThe system learning in real time<\/h2>\nThese animations show how the model would process new information every two trading weeks. The most interesting part is watching the red shading and uncertainty spikes appear before<\/em> price drops \u2014 not as hindsight analysis, but as live signals.<\/p>\n<\/section>\n\n<\/ul>\n<\/div>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F6.gif”<\/a>; title_text=”F6″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 6 \u00b7 ANIMATION \u00b7 SPY PRICE WITH GROWING WINDOW<\/div>\nEach frame adds 14 trading days. Watch how the Risk-Off shading covers stress periods before they’re confirmed by the final price of each period<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F7.gif”<\/a>; title_text=”F7″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 7 \u00b7 ANIMATION \u00b7 UNCERTAINTY \u03a3*(T) WITH GROWING WINDOW<\/div>\nThe red peaks above the 0.65 threshold are the signal that the model has entered unknown territory. Notice how after each major transition, \u03a3*(t) drops quickly \u2014 the model re-learns the new market structure and returns to operating with confidence.<\/p>\n <\/p>\nThe signals in practice<\/h2>\n <\/p>\n <\/p>\n\n\n\nCONDITION<\/th>\nREGIME<\/th>\nACTION<\/th>\nWHY<\/th>\n<\/tr>\n<\/thead>\n\n\nP(t) < 0.60 AND \u03a3*(t) < 0.65<\/td>\nRisk-On<\/span><\/strong><\/span><\/td>\nFull position<\/td>\nModel is convinced we’re in expansion and operating on familiar ground<\/td>\n<\/tr>\n\nP(t) > 0.60<\/td>\nRisk-Off<\/span><\/strong><\/td>\nReduce 40\u201360%<\/td>\nCross-asset signals indicate the cycle is turning contractionary<\/td>\n<\/tr>\n\n\u03a3*(t) > 0.65<\/td>\nRisk-Off<\/span><\/strong><\/span><\/td>\nReduce 40\u201360%<\/td>\nModel is in unknown territory \u2014 something is changing even if it can’t name what<\/td>\n<\/tr>\n\nP(t) > 0.60 AND \u03a3*(t) > 0.65<\/td>\nMaximum caution<\/span><\/strong><\/span><\/td>\n25% position<\/td>\nModel is both convinced of stress and confused at the same time \u2014 worst case combination<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nREADING THE SIGNALS DAY TO DAY<\/div>\n\nFirst alert:<\/strong> \u03a3*(t) rises above 0.65 but P(t) is still below 0.60. The model is detecting that something is breaking before it can say what. Cut to half position.<\/li>\nRisk-Off confirmation:<\/strong> P(t) clears 0.60 and holds there for 5 consecutive days. Not noise \u2014 the cycle is turning. Adjust size based on where \u03a3*(t) is sitting.<\/li>\nMaximum confusion zone:<\/strong> P(t) oscillates rapidly around 0.50 while \u03a3*(t) is rising. The model doesn’t know which regime is coming. Reduce gross exposure regardless of direction.<\/li>\nBack to Risk-On:<\/strong> P(t) below 0.45 for 5 days and \u03a3*(t) declining for 10. The model is finding its footing again. Scale back up gradually.<\/li>\nMaximum conviction:<\/strong> P(t) < 0.35 and \u03a3*(t) < 0.40. The model is fully comfortable with the current regime. Full position with wide stops justified by low structural uncertainty.<\/li>\n<\/ul>\nWhat this model can’t do<\/h2>\n\n\n01<\/div>\nWeights are hand-coded<\/div>\nThe model weights reflect economic logic, not statistical optimisation. A model trained on labelled historical cycles would likely perform better out-of-sample.<\/p>\n<\/div>\n\n02<\/div>\n\u03a3*(t) is an approximation<\/div>\nTrue Bayesian uncertainty requires MC-Dropout or full variational inference. This version captures the transition signal but not the exact posterior distribution.<\/p>\n<\/div>\n\n03<\/div>\nNo precise entry timing<\/div>\nThe model tells you what conditions the market is in, not exactly when a reversal will happen. A high-uncertainty regime can persist for weeks before resolving.<\/p>\n\nThe best signal a model can give you isn’t what it thinks will happen. It’s when it tells you it doesn’t know \u2014 because that’s the most honest thing it can do.<\/em><\/span><\/p>\n<\/div>\n<\/div>\n[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” parallax__hover=”off” parallax_method__hover=”on” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/gestion-patrimonial-pro-investing.png”<\/a>; title_text=”wealth management pro” url=”https:\/\/proinvesting.teachable.com\/p\/investor1″<\/a>; align_tablet=”center” align_phone=”center” align_last_edited=”on|desktop” _builder_version=”4.18.0″ border_radii=”on|5px|5px|5px|5px” border_color_all=”RGBA(255,255,255,0)” global_colors_info=”{}” border_radii__hover=”on||||” border_radii__hover_enabled=”on||||”][\/et_pb_image][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/2-1.png”<\/a>; title_text=”financial education” 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We built a system that detects economic cycle shifts before price confirms them. The edge isn’t in what the model predicts \u2014 it’s in the moments when it admits it has no idea. The problem classic models ignore Most risk tools measure how much the market moved. […]<\/p>\n","protected":false},"author":6,"featured_media":8122,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_seo_schema_type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[29],"tags":[],"class_list":["post-8109","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-quant-research"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg?fit=2000%2C571&ssl=1","jetpack-related-posts":[],"jetpack_shortlink":"https:\/\/wp.me\/pcZIdA-26N","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/comments?post=8109"}],"version-history":[{"count":18,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions"}],"predecessor-version":[{"id":8141,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions\/8141"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media\/8122"}],"wp:attachment":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media?parent=8109"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/categories?post=8109"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/tags?post=8109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}](\"https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/Screenshot-2026-04-20-at-8.21.14-PM.png”\"){kind=link}
![https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F6.gif”<\/a>; title_text=”F6″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 6 \u00b7 ANIMATION \u00b7 SPY PRICE WITH GROWING WINDOW<\/div>\nEach frame adds 14 trading days. Watch how the Risk-Off shading covers stress periods before they’re confirmed by the final price of each period<\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F7.gif”<\/a>; title_text=”F7″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 7 \u00b7 ANIMATION \u00b7 UNCERTAINTY \u03a3*(T) WITH GROWING WINDOW<\/div>\nThe red peaks above the 0.65 threshold are the signal that the model has entered unknown territory. Notice how after each major transition, \u03a3*(t) drops quickly \u2014 the model re-learns the new market structure and returns to operating with confidence.<\/p>\n <\/p>\nThe signals in practice<\/h2>\n <\/p>\n <\/p>\n\n\n\nCONDITION<\/th>\nREGIME<\/th>\nACTION<\/th>\nWHY<\/th>\n<\/tr>\n<\/thead>\n\n\nP(t) < 0.60 AND \u03a3*(t) < 0.65<\/td>\nRisk-On<\/span><\/strong><\/span><\/td>\nFull position<\/td>\nModel is convinced we’re in expansion and operating on familiar ground<\/td>\n<\/tr>\n\nP(t) > 0.60<\/td>\nRisk-Off<\/span><\/strong><\/td>\nReduce 40\u201360%<\/td>\nCross-asset signals indicate the cycle is turning contractionary<\/td>\n<\/tr>\n\n\u03a3*(t) > 0.65<\/td>\nRisk-Off<\/span><\/strong><\/span><\/td>\nReduce 40\u201360%<\/td>\nModel is in unknown territory \u2014 something is changing even if it can’t name what<\/td>\n<\/tr>\n\nP(t) > 0.60 AND \u03a3*(t) > 0.65<\/td>\nMaximum caution<\/span><\/strong><\/span><\/td>\n25% position<\/td>\nModel is both convinced of stress and confused at the same time \u2014 worst case combination<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nREADING THE SIGNALS DAY TO DAY<\/div>\n\nFirst alert:<\/strong> \u03a3*(t) rises above 0.65 but P(t) is still below 0.60. The model is detecting that something is breaking before it can say what. Cut to half position.<\/li>\nRisk-Off confirmation:<\/strong> P(t) clears 0.60 and holds there for 5 consecutive days. Not noise \u2014 the cycle is turning. Adjust size based on where \u03a3*(t) is sitting.<\/li>\nMaximum confusion zone:<\/strong> P(t) oscillates rapidly around 0.50 while \u03a3*(t) is rising. The model doesn’t know which regime is coming. Reduce gross exposure regardless of direction.<\/li>\nBack to Risk-On:<\/strong> P(t) below 0.45 for 5 days and \u03a3*(t) declining for 10. The model is finding its footing again. Scale back up gradually.<\/li>\nMaximum conviction:<\/strong> P(t) < 0.35 and \u03a3*(t) < 0.40. The model is fully comfortable with the current regime. Full position with wide stops justified by low structural uncertainty.<\/li>\n<\/ul>\nWhat this model can’t do<\/h2>\n\n\n01<\/div>\nWeights are hand-coded<\/div>\nThe model weights reflect economic logic, not statistical optimisation. A model trained on labelled historical cycles would likely perform better out-of-sample.<\/p>\n<\/div>\n\n02<\/div>\n\u03a3*(t) is an approximation<\/div>\nTrue Bayesian uncertainty requires MC-Dropout or full variational inference. This version captures the transition signal but not the exact posterior distribution.<\/p>\n<\/div>\n\n03<\/div>\nNo precise entry timing<\/div>\nThe model tells you what conditions the market is in, not exactly when a reversal will happen. A high-uncertainty regime can persist for weeks before resolving.<\/p>\n\nThe best signal a model can give you isn’t what it thinks will happen. It’s when it tells you it doesn’t know \u2014 because that’s the most honest thing it can do.<\/em><\/span><\/p>\n<\/div>\n<\/div>\n[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” parallax__hover=”off” parallax_method__hover=”on” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/gestion-patrimonial-pro-investing.png”<\/a>; title_text=”wealth management pro” url=”https:\/\/proinvesting.teachable.com\/p\/investor1″<\/a>; align_tablet=”center” align_phone=”center” align_last_edited=”on|desktop” _builder_version=”4.18.0″ border_radii=”on|5px|5px|5px|5px” border_color_all=”RGBA(255,255,255,0)” global_colors_info=”{}” border_radii__hover=”on||||” border_radii__hover_enabled=”on||||”][\/et_pb_image][et_pb_image src=”https:\/\/proinvesting.co\/wp-content\/uploads\/2025\/06\/2-1.png”<\/a>; title_text=”financial education” 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We built a system that detects economic cycle shifts before price confirms them. The edge isn’t in what the model predicts \u2014 it’s in the moments when it admits it has no idea. The problem classic models ignore Most risk tools measure how much the market moved. […]<\/p>\n","protected":false},"author":6,"featured_media":8122,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_seo_schema_type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[29],"tags":[],"class_list":["post-8109","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-quant-research"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F2-1.jpeg?fit=2000%2C571&ssl=1","jetpack-related-posts":[],"jetpack_shortlink":"https:\/\/wp.me\/pcZIdA-26N","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/comments?post=8109"}],"version-history":[{"count":18,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions"}],"predecessor-version":[{"id":8141,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/posts\/8109\/revisions\/8141"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media\/8122"}],"wp:attachment":[{"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/media?parent=8109"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/categories?post=8109"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/proinvesting.co\/es\/wp-json\/wp\/v2\/tags?post=8109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}](\"https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F6.gif”\"){kind=link}
![https:\/\/proinvesting.co\/wp-content\/uploads\/2026\/04\/F7.gif”<\/a>; title_text=”F7″ _builder_version=”4.18.0″ _module_preset=”default” border_radii=”on|20px|20px|20px|20px” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nFIGURE 7 \u00b7 ANIMATION \u00b7 UNCERTAINTY \u03a3*(T) WITH GROWING WINDOW<\/div>\nThe red peaks above the 0.65 threshold are the signal that the model has entered unknown territory. Notice how after each major transition, \u03a3*(t) drops quickly \u2014 the model re-learns the new market structure and returns to operating with confidence.<\/p>\n <\/p>\nThe signals in practice<\/h2>\n <\/p>\n <\/p>\n\n\n\nCONDITION<\/th>\nREGIME<\/th>\nACTION<\/th>\nWHY<\/th>\n<\/tr>\n<\/thead>\n\n\nP(t) < 0.60 AND \u03a3*(t) < 0.65<\/td>\nRisk-On<\/span><\/strong><\/span><\/td>\nFull position<\/td>\nModel is convinced we’re in expansion and operating on familiar ground<\/td>\n<\/tr>\n\nP(t) > 0.60<\/td>\nRisk-Off<\/span><\/strong><\/td>\nReduce 40\u201360%<\/td>\nCross-asset signals indicate the cycle is turning contractionary<\/td>\n<\/tr>\n\n\u03a3*(t) > 0.65<\/td>\nRisk-Off<\/span><\/strong><\/span><\/td>\nReduce 40\u201360%<\/td>\nModel is in unknown territory \u2014 something is changing even if it can’t name what<\/td>\n<\/tr>\n\nP(t) > 0.60 AND \u03a3*(t) > 0.65<\/td>\nMaximum caution<\/span><\/strong><\/span><\/td>\n25% position<\/td>\nModel is both convinced of stress and confused at the same time \u2014 worst case combination<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nREADING THE SIGNALS DAY TO DAY<\/div>\n\nFirst alert:<\/strong> \u03a3*(t) rises above 0.65 but P(t) is still below 0.60. The model is detecting that something is breaking before it can say what. Cut to half position.<\/li>\nRisk-Off confirmation:<\/strong> P(t) clears 0.60 and holds there for 5 consecutive days. Not noise \u2014 the cycle is turning. Adjust size based on where \u03a3*(t) is sitting.<\/li>\nMaximum confusion zone:<\/strong> P(t) oscillates rapidly around 0.50 while \u03a3*(t) is rising. The model doesn’t know which regime is coming. Reduce gross exposure regardless of direction.<\/li>\nBack to Risk-On:<\/strong> P(t) below 0.45 for 5 days and \u03a3*(t) declining for 10. The model is finding its footing again. Scale back up gradually.<\/li>\nMaximum conviction:<\/strong> P(t) < 0.35 and \u03a3*(t) < 0.40. The model is fully comfortable with the current regime. Full position with wide stops justified by low structural uncertainty.<\/li>\n<\/ul>\nWhat this model can’t do<\/h2>\n\n\n01<\/div>\nWeights are hand-coded<\/div>\nThe model weights reflect economic logic, not statistical optimisation. A model trained on labelled historical cycles would likely perform better out-of-sample.<\/p>\n<\/div>\n\n02<\/div>\n\u03a3*(t) is an approximation<\/div>\nTrue Bayesian uncertainty requires MC-Dropout or full variational inference. This version captures the transition signal but not the exact posterior distribution.<\/p>\n<\/div>\n\n03<\/div>\nNo precise entry timing<\/div>\nThe model tells you what conditions the market is in, not exactly when a reversal will happen. A high-uncertainty regime can persist for weeks before resolving.<\/p>\n\nThe best signal a model can give you isn’t what it thinks will happen. 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We built a system that detects economic cycle shifts before price confirms them. The edge isn’t in what the model predicts \u2014 it’s in the moments when it admits it has no idea. The problem classic models ignore Most risk tools measure how much the market moved. 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