Six layers. 24–30 weeks. The institutional framework used by Druckenmiller, Dalio, and Paul Tudor Jones — built for serious practitioners.
This curriculum is designed for the serious practitioner — someone willing to spend 6–12 months building a repeatable, institutional-quality macro framework. You are not here to pick stocks. You are here to understand the world, identify regime shifts before consensus, and express those views across asset classes with disciplined risk management. This is the curriculum that does not exist at universities. It lives on trading desks, in hedge fund training programs, and in the hard-won experience of practitioners who have survived multiple cycles.
"The most important thing in investing is to determine the macro environment."
— Howard MarksThe foundation of everything. Before you look at a single trade, you must know where you are in the economic cycle — whether credit is expanding or contracting, and whether inflation is accelerating or decelerating. Every other layer is interpreted through this lens. Get the regime wrong, and even your best trades will work against you.
The economy moves in 5–10 year cycles. Early expansion favours equities and credit. Late expansion favours commodities. Contraction favours government bonds and cash. The key is identifying transitions — by the time everyone agrees we're in recession, the market has already priced it.
Credit is the lifeblood of the modern economy. Ray Dalio distinguishes the short-term debt cycle (5–8 years) from the long-term debt cycle (75–100 years). The credit impulse — the rate of change of credit growth — often leads GDP by 9–12 months.
The Bridgewater four-quadrant framework: Rising Growth/Rising Inflation → commodities, value, short bonds. Rising Growth/Falling Inflation → equities, growth, long bonds. Falling Growth/Rising Inflation → cash, gold, TIPS (the worst environment). Falling Growth/Falling Inflation → government bonds, defensives.
GDP is reported with a lag. Professionals focus on leading indicators: the yield curve (inverts 12–18 months before recession), ISM New Orders (leads industrial production 3–6 months), initial jobless claims (weekly, real-time), and the Conference Board LEI.
When the economy runs above potential (positive output gap), inflation rises. Below potential, inflation falls. Real skill: making your own assessment using the quits rate, capacity utilisation, and productivity trends — not waiting for the CBO.
Cyclical trends last 1–5 years (business cycle driven). Secular trends last 10–30 years (demographics, debt supercycles, structural policy). Knowing whether you're operating with or against the secular trend changes your conviction and time horizon.
The most profitable trades occur at regime transitions. Watch for: yield curve inversions/steepening, divergence between leading and lagging indicators, central bank policy pivots, and credit spread widening. Druckenmiller calls identifying these inflection points the single most important skill in macro.
Deep study of the business cycle as practitioners use it, not academics. We trace 2001, 2008, 2020, and 2022 from early expansion through peak, contraction, and trough. For each phase we document which indicators turned first and where consensus was wrong. You will learn the ECRI framework and build your own cycle clock.
Dalio's credit cycle in operational detail. We study the Senior Loan Officer Opinion Survey (SLOOS), bank lending standards, high-yield credit spreads, the Fed's Z.1 Flow of Funds, and private credit markets. We examine how the 2007 credit contraction was visible 12 months before Lehman through CDX and ABX indices.
We build the four-quadrant framework and study the 1970s stagflation, the 2010s disinflation, and the 2021–2023 inflation shock. You will learn to read PCE, CPI, trimmed-mean CPI, Cleveland Fed Median CPI, and the Truflation real-time index.
Construct a 15-indicator regime dashboard using freely available data from FRED, ISM, and BLS. The dashboard classifies the current environment across three dimensions: growth (accelerating/decelerating), inflation (rising/falling), and policy (tightening/easing). This becomes your north star for the rest of the course.
We study five profitable regime transitions: the 2000 tech bubble peak, the 2003 recovery, the 2007–2008 financial crisis, the 2020 COVID crash and recovery, and the 2022 inflation shock. For each: earliest signals, the wrong consensus narrative, and the trades that captured the transition.
Using FRED data, classify each quarter from Q1 2000 to Q4 2025 into one of the four quadrants. For each quarter, list ISM Manufacturing PMI, Core PCE YoY, 10Y Treasury yield, and S&P 500 quarterly return. Identify five quarters where the regime shifted and document the earliest signal. Submit as a spreadsheet with commentary.
Download total credit to the non-financial sector from the BIS database for the US, China, and Eurozone. Calculate the credit impulse (12-month change in credit flow as % of GDP) for each. Plot all three from 2005 to present. Identify periods where the impulse led GDP and asset prices. Write a one-page analysis of the current reading and its implications.
Build your 15-indicator dashboard in a spreadsheet. Update it with the latest available data. Produce a one-page "Macro Regime Report" classifying the current environment, identifying the most likely regime transition ahead, and listing 3–5 asset class implications. Update monthly for the remainder of the course.
Read the latest Wall Street consensus outlook (Goldman Sachs, JP Morgan, or Morgan Stanley year-ahead report). Identify their base case. Now construct the strongest possible counter-argument using your dashboard. What would have to happen for consensus to be wrong? What indicators would confirm the counter-consensus view?
"Liquidity is everything. When you have it, everything works. When you don't, nothing works."
— Stanley DruckenmillerIf the macro regime is the map, liquidity is the fuel. The single most reliable driver of asset prices over 6–18 month horizons is not earnings, not GDP, not sentiment — it is the availability and direction of liquidity. This module teaches you to track the money: where it is being created, where it is flowing, and when it is being withdrawn.
Net liquidity = Fed balance sheet minus RRP minus TGA. This net figure has correlated with the S&P 500 more reliably than any fundamental metric since 2009. Track it weekly via the Fed's H.4.1 release every Thursday.
The 2s10s spread signals recession when inverted. The 3m/10Y is the Fed's preferred recession indicator. Bear steepening (long rates rising faster) = inflation fears. Bull steepening (short rates falling faster) = Fed cutting, market expecting recovery.
The DXY is the single most important price in global macro. Roughly $13 trillion in dollar-denominated debt exists outside the US. Dollar strength tightens global financial conditions, pressures EM debt, suppresses commodities, and slows global trade.
Markets move on relative monetary policy, not absolute levels. The BOJ's yield curve control policy (2016–2024) made the yen a global carry funding currency. When BOJ tightened in 2024, the unwind sent shockwaves through every asset class.
When Japanese rates are near zero and US rates are 5%, capital borrows in yen and invests in US assets. These flows can exceed $1 trillion notional. When they unwind — due to BOJ tightening or a volatility spike — the effect is sudden and violent.
The most important source of global dollar liquidity is not the Fed — it is the offshore eurodollar banking system. When this contracts (2008, 2011, 2020), dollar funding stress appears in the FX swap basis, the TED spread, and cross-currency basis swaps.
The Treasury General Account and Reverse Repo Facility create "shadow" QE/QT. When the TGA rises (Treasury issuing debt), it drains reserves. When TGA falls (Treasury spending), it adds reserves. Hundreds of billions can shift without making headlines.
Walk through the Fed's weekly H.4.1 release line by line. Learn to identify total balance sheet size, composition (Treasuries, MBS, lending facilities), and calculate net liquidity. We trace QE1, QE2, QE3, QE4, and both QT cycles — overlaying the S&P 500 and credit spreads to see the correlation in real time.
Complete guide to the yield curve as a macro signal: level, slope (2s10s, 3m10Y), and curvature (butterfly spreads). We study every yield curve inversion since 1970 and subsequent recession timing. The four curve regimes — bull steepening, bear steepening, bull flattening, bear flattening — and which assets outperform in each.
Why the DXY matters for everything. We trace the dollar bull markets of 2014–2016 and 2021–2022, documenting the carnage in EM equities, currencies, and commodities. We learn to monitor the Goldman Sachs FCI and the Chicago Fed NFCI.
Build a "central bank policy matrix" covering Fed, ECB, BOJ, PBOC, and BOE. For each: current rate, direction, balance sheet trajectory, forward guidance, and market pricing (OIS curves, fed funds futures). Study how Fed-ECB divergence drove EUR/USD to parity in 2022.
Construct a composite index using: (1) Fed net liquidity, (2) global CB balance sheets, (3) US Financial Conditions Index, (4) credit spread levels, (5) FX swap basis, (6) SLOOS bank lending standards, (7) M2 money supply growth. Historically, being long risk when positive and defensive when negative has outperformed buy-and-hold.
Anatomy of carry trade buildup and unwind. The yen carry trade (2012–2024), the SNB floor carry (pre-2015), the EM carry (2016–2018). Primary case study: the August 2024 yen carry unwind — BOJ decision, yen move, Nikkei crash, contagion to US tech.
Build a weekly-updating spreadsheet tracking Fed net liquidity (balance sheet − TGA − RRP). Overlay the S&P 500. Calculate rolling 6-month, 1-year, and 3-year correlations. Write a one-page memo: when does this relationship break down? What other factors override liquidity?
Using FRED data for 2Y and 10Y Treasury yields from 1990 to present, classify each month into one of four regimes. For each regime, calculate average monthly returns for S&P 500, long Treasuries, gold, and DXY. Present as a table and write a trading playbook for each regime.
Build a one-page dashboard covering the Fed, ECB, BOJ, PBOC, and BOE. For each: current rate, last action, next meeting, balance sheet trend, and your assessment of next move. Update weekly for 8 weeks. Review your prediction accuracy.
Research and write a 3–5 page case study on the August 2024 carry trade unwind. Cover: the buildup (2022–2024), BOJ's policy shift, speed of the yen move, the contagion path (Nikkei → US tech → global risk), and the resolution. What leading indicators could have warned you?
"The most dangerous thing in markets is a story that stops being confirmed by the data."
— Paul Tudor JonesMarkets do not move in isolation. Bonds talk to equities. Copper talks to growth. Gold talks to real yields. Oil talks to inflation. Credit spreads talk to risk appetite. The professional macro investor reads these conversations constantly, looking for confirmation — and, more importantly, for divergence. When stocks are making new highs but credit spreads are widening, someone is wrong. It's usually equities.
Copper's demand is driven by construction, manufacturing, and infrastructure — making it one of the purest cyclical growth indicators. The copper/gold ratio tracks the 10-year Treasury yield almost perfectly because it captures the growth-versus-safety preference in real time.
Gold does not care about nominal rates. It cares about real yields — the interest rate after subtracting inflation. The 10-year TIPS yield is gold's primary driver. From 2018–2020, as real yields fell from +1% to −1%, gold rallied from $1,200 to $2,070.
Credit markets are smarter than equity markets. High-yield spreads typically widen 3–6 months before equity market peaks. In 2007, CDX started widening in February — seven months before the S&P peaked. If you only watch one non-equity market, watch credit.
Rising oil from strong demand is growth-positive. Rising oil from supply disruption is stagflationary and equity-negative. Always decompose oil moves into demand-driven vs. supply-driven components using the crack spread and futures curve shape (contango vs. backwardation).
A sustainable equity rally should be confirmed by: tightening credit spreads, rising copper, stable/weakening dollar, rising breakevens, and a steepening yield curve. When these start diverging, the rally is losing its foundation. John Murphy's intermarket analysis is the classic reference.
If equities and credit spreads diverge for more than 4–6 weeks, one must capitulate to the other. Historically, credit is right about 70% of the time. Copper vs. equities: copper is right about 65% of the time. The system identifies fat pitch situations — where the odds are skewed.
The 5Y5Y forward breakeven represents expected inflation 5 years from now for the following 5 years — the Fed's preferred measure of long-term inflation expectations. When breakevens collapsed in Q4 2022, they signalled the inflation peak 3 months before CPI confirmed it.
Build a rolling correlation matrix for S&P 500, 10Y Treasury yield, DXY, gold, copper, WTI, high-yield spreads, and VIX. Study how correlations shift across regimes — in risk-off environments, the equity/bond correlation often flips. Identify which divergences matter and which are noise.
Deep dive into copper, oil, gold, and the Bloomberg Commodity Index as macro indicators. We study the futures curve for each — contango vs. backwardation. We examine the Baltic Dry Index as a global trade proxy. This lesson will permanently change how you read a commodity price chart.
Study the credit market's predictive power through 2000, 2007, 2015, 2018, and 2020. Learn to monitor CDX IG/HY, iTraxx Europe, HYG/JNK ETF flows, ICE BofA HY OAS, and leveraged loan markets. Build a credit stress dashboard with 8 indicators and a traffic-light scoring system.
Demystify the TIPS market. Learn how TIPS work mechanically, what breakeven inflation means, and why the 5Y5Y forward is the Fed's preferred gauge. Real yields as the "cleanest" macro signal — when they rise sharply, almost everything (gold, equities, housing, crypto) comes under pressure.
Formalise divergence detection into a repeatable process. The key cross-asset pairs: (1) S&P 500 vs. HY spreads, (2) S&P 500 vs. copper, (3) Gold vs. real yields, (4) DXY vs. commodity index, (5) Russell 2000/S&P 500 ratio, (6) equity vol vs. credit vol. Build a weekly divergence checklist.
Practice synthesising cross-asset signals into a single coherent narrative. Three historical case studies: March 2009 bottom, January 2018 "vol-mageddon," October 2022 bottom. You will learn to write a weekly "Cross-Asset Note" — a one-page document that synthesises all signals into a single view.
Download daily data for copper, gold, and the 10Y Treasury yield from 2010 to present. Calculate the copper/gold ratio and plot it against the 10Y yield. Identify periods where the ratio diverged by more than 1 standard deviation and document what happened next. Is this relationship still valid?
Using ICE BofA HY OAS and the S&P 500, identify every period from 2005–2025 where HY spreads widened more than 50bps over 30 days while the S&P was flat or positive. Document: how long each divergence lasted, which market was "right," and the magnitude of the resolution. Calculate your win rate if you had shorted equities at each signal.
For 8 consecutive weeks, write a one-page "Cross-Asset Signal Report" covering: the story copper is telling, the story credit is telling, the story gold/real yields are telling, any active divergences, and your overall macro narrative. By week 8 you should produce this in under 30 minutes.
Take three major oil moves (20%+ in WTI from the past 5 years). For each, decompose: demand-driven or supply-driven? Use evidence from: futures curve shape, crack spreads, global PMIs, OPEC production data, and inventory reports. Document how equities, bonds, and the dollar responded to each.
"Be fearful when others are greedy, and greedy when others are fearful."
— Warren BuffettYou can get the macro right, the liquidity right, and the cross-asset signals right — and still lose money if you are on the same side as everyone else. Crowded trades unwind violently. Consensus positioning creates the conditions for its own reversal. This module teaches you to read the crowd: where money is concentrated, what everyone believes, and when the trade is so crowded that the slightest catalyst can trigger a stampede for the exit.
Published every Friday by the CFTC, the COT report shows net positioning of Commercials (smart money), Large Speculators (hedge funds/CTAs), and Small Speculators (retail). When large specs are at extreme positions (top/bottom 10% of the 3-year range), the trend is nearing exhaustion.
The VIX is not just a fear gauge — it is a market with its own term structure. Extreme contango signals complacency (prepare for a shock). Extreme backwardation signals panic (prepare for a reversal). The VVIX — volatility of VIX — adds a further layer of uncertainty measurement.
The AAII survey (retail), Investors Intelligence (newsletter writers), and Conference Board Consumer Confidence each capture different slices of psychology. At extremes — AAII bulls above 55% or below 20% — they become powerful contrarian indicators. When everyone is bullish, they are already invested. There are no marginal buyers left.
The Bank of America Global Fund Manager Survey (monthly) shows where institutional money is concentrated. When equity funds see 10+ consecutive weeks of inflows, the market is typically near a short-term top. BofA cash levels above 5% are contrarian bullish; below 4% are contrarian bearish.
When the CBOE equity put/call ratio spikes above 1.2, it signals excessive hedging demand (contrarian bullish). Below 0.6 signals excessive complacency (contrarian bearish). But the raw ratio can mislead — also consider the dollar volume, open interest concentration at key strikes, and dealer gamma.
When dealers are "long gamma," their hedging activity suppresses volatility — they buy dips and sell rips. When dealers are "short gamma," their hedging amplifies moves — they must sell into falling markets. Understanding dealer gamma explains why markets sometimes trade in tight ranges for weeks and then explode.
Three stages: (1) Narrative cracks — a data point challenges the thesis but positioning doesn't change. (2) First movers exit — dismissed as profit-taking. (3) Stampede — stop losses hit, margin calls triggered, self-reinforcing unwind. Speed is proportional to crowdedness. Subprime's unwind was catastrophic because the long position was so crowded.
Download, clean, and interpret the CFTC COT report. Focus on net speculative positioning in S&P 500 futures, 10Y Treasury futures, EUR/USD, JPY/USD, gold, crude, and copper. Calculate z-scores (how extreme is current positioning vs. 3-year range). Build a COT "extremes" dashboard. Study 10 historical cases where extremes preceded major reversals — and 5 where they did not.
Dissect the VIX complex: spot VIX, VIX futures curve, VIX options, and VIX ETPs (VXX, SVXY, UVXY). Calculate the VIX term structure slope and plot it historically. Study the volatility regime framework and the February 2018 "Volmageddon" mechanics — how the VIX ETP structure created a self-reinforcing collapse.
Build a multi-input sentiment composite: AAII survey, Investors Intelligence, CNN Fear & Greed, put/call ratios (5-day smoothed), VIX vs. realised volatility, and BofA FMS cash levels. Score each input from −2 (extreme fear) to +2 (extreme greed). Composite score from −12 to +12. Backtest against S&P 500 forward returns.
Read the BofA Global FMS, EPFR fund flow data, and ETF creation/redemption patterns. Build a "consensus positioning map" showing where institutional money is overweight, underweight, and neutral. Study how to identify the "most crowded trade" each quarter — historically it underperforms over the next 6 months about 60% of the time.
Learn the mechanics of market maker delta hedging, gamma exposure, and its impact on realised volatility. Examine the January 2021 GameStop squeeze (retail call buying forced dealer hedging into an illiquid stock) and the September 2020 "Nasdaq whale" (SoftBank's massive call buying amplified the tech rally through dealer gamma). Some market moves have nothing to do with fundamentals.
The hardest lesson. We study the psychology of contrarian investing — why it is so difficult to buy when everyone is selling. PTJ: "I'm always thinking about losing money as opposed to making money." Howard Marks's "second-level thinking." Develop personal rules for acting at extremes.
Build a spreadsheet tracking net speculative positioning (z-score of 3-year range) for S&P 500, 10Y Treasuries, EUR/USD, gold, and crude. Update weekly. Highlight readings above +1.5 or below −1.5 standard deviations. Over 8 weeks, track how many extreme readings resolved with a reversal vs. continuation.
Build the sentiment composite from Lesson 4.3. Calculate the current reading. Track it weekly for 8 weeks alongside S&P 500 performance. Did extreme readings (if any) provide useful signals? How would you refine the composite based on live experience?
Select three consensus trades that blew up in the last 5 years (e.g., long ARKK in 2021, short volatility pre-COVID, long China reopening in 2023). For each, document: the justifying narrative, the positioning data showing crowdedness, the catalyst that started the unwind, and the signal that would have gotten you out early.
Each day for 30 days, identify the dominant market narrative from financial media and trading desks. Write the consensus view and the strongest counter-argument. Do not trade on these — just observe. At the end: how often was consensus right? What patterns do you notice about when consensus is most vulnerable?
"In macro, you have to be right about the world, not just about the market."
— Stanley DruckenmillerEven the most rigorous macro framework can be undone by a binary event you didn't size around. This module teaches you to live by the calendar — to understand that FOMC meetings, CPI prints, elections, and geopolitical shocks are not interruptions to your framework, they are inputs to it. You never hold full size into a binary event. You size down, buy optionality, or sit out.
Professional macro investors live by their calendar. It is not just a list of dates — it is a probability-weighted schedule of potential regime-changing events. The calendar includes: FOMC meetings, CPI/PCE/NFP releases, PMI surveys, central bank speeches (Jackson Hole, Davos), and political events (elections, budget announcements, sanctions).
Before every major event, the professional builds a scenario tree. For an FOMC meeting: What is the base case (consensus-priced)? What is the hawkish surprise? What is the dovish surprise? For each, what happens to the 2-year yield, the DXY, and the S&P 500? Assign probabilities and calculate the expected impact on your portfolio.
Markets move on news relative to expectations. A CPI print of 3.2% is bullish if consensus expected 3.6%, and bearish if consensus expected 2.9%. The skill is understanding not just what the data will say, but what the market is priced for — and sizing based on the gap between your forecast and the priced outcome.
Most events have asymmetric payoff structures. If the market rallied 5% into an FOMC meeting on rate-cut hopes, the upside from a dovish surprise is limited (already priced) but the downside from a hawkish surprise is large (repricing required). This asymmetry — more downside than upside — justifies reducing size or buying puts before the event.
When Russia invaded Ukraine in February 2022, the first-order effect was obvious (energy prices up, European equities down). The second-order effects were more important: European energy dependency shifted capital flows, Germany's industrial model was structurally challenged, and NATO's defence spending trajectory changed permanently. Second-order thinking is where the macro edge lives.
When you cannot determine the direction of a binary event but need to maintain a position through it, options allow you to define your maximum loss while preserving upside. Buying straddles (both calls and puts) before high-uncertainty events profits from large moves in either direction. The key is buying before implied volatility spikes.
We construct a 12-month global macro event calendar covering: FOMC, ECB, BOJ, BOE meetings; US CPI/PCE/NFP releases; global PMI survey dates; G7/G20 summits; Jackson Hole symposium; major elections; and geopolitical flashpoints. You will maintain this calendar throughout the course, updating it with market expectations and outcomes.
We dissect every FOMC meeting component: the statement, the dot plot, the Summary of Economic Projections (SEP), and the press conference. We study how to position before meetings using options, how to read real-time market reaction to identify the "true" surprise, and how to fade knee-jerk reactions when they overshoot. Case studies: March 2022 (first hike), September 2022 (75bps shock), November 2023 (pivot signal).
The ritual of every macro desk before a major data release: estimate the whisper number vs. consensus, map out the four scenarios (beat/miss for headline and core), assign probabilities, and define position adjustments for each scenario. We study how to read CPI decomposition (shelter, services ex-shelter, goods) and NFP internals (AHE, breadth, revisions) in real time.
We build a framework for analysing geopolitical events through macro channels: (1) Energy prices and inflation, (2) Supply chain disruption and manufacturing, (3) Capital flows and safe-haven demand, (4) Trade policy and currency effects, (5) Defence spending and fiscal policy shifts. Case studies: Ukraine war (2022), China-Taiwan tensions, US-China trade war (2018–2019), OPEC+ production cuts.
We learn the practical toolkit: buying calls/puts for directional bets with defined risk, buying straddles/strangles for high-uncertainty events, selling volatility after events when implied volatility is elevated relative to realised, and using VIX options to hedge portfolio tail risk. We discuss the PTJ rule: never hold full size into binary events.
Build a complete macro event calendar for the next 90 days. For the next FOMC meeting, write a "Pre-Meeting Brief" covering: current market pricing (OIS curve), the three most likely scenarios, the consensus expectation, your personal forecast, and your planned position adjustment for each scenario. Submit before the meeting. Review after.
Before the next CPI release, write a scenario analysis: define the whisper number, map four outcomes (big beat, small beat, small miss, big miss), assign probabilities, and describe how each would affect the 2-year yield, DXY, S&P 500, and gold. After the release, compare the actual outcome to your scenarios. Was your scenario tree comprehensive?
Write a 3–5 page case study on the macro impact of Russia's invasion of Ukraine in February 2022. Cover: (1) The first-order effects (energy, wheat, defence stocks), (2) The second-order effects (European energy transition, German industrial restructuring, NATO spending), (3) The third-order effects (EM food inflation, Russia sanctions circumvention, China's policy signals). What trades did these effects support?
Choose one position in your paper portfolio (from the Capstone project or a hypothetical) that is exposed to an upcoming event. Write a pre-event risk management plan: What is the event? What is your exposure? What are the scenarios? How are you adjusting size, using options, or repositioning? After the event, document whether your plan worked and what you would change.
"Rule number one: don't lose money. Rule number two: don't forget rule number one."
— Warren BuffettRisk management is always last — but never optional. PTJ's rule: never risk more than 1–2% of the book per trade, always know your exit before entry, always have dry powder. Alpha comes and goes. Strategies decay. Markets evolve. The ability to survive and compound is the only permanent edge. This module is about staying in the game long enough for the other five layers to work.
If you risk 2% per trade with a 40% win rate and a 3:1 reward-to-risk ratio, you compound at roughly 14% per year. If you risk 10% per trade with the same edge, you will blow up within 20 trades. The 1–2% rule is not conservative — it is the mathematically optimal path to long-term compounding given realistic win rates.
Individual position sizing means nothing if all your positions are correlated. If you have 10 "independent" 2% risk trades, but they are all long risk assets correlated at 0.7, your actual portfolio risk is not 2% — it is much closer to 20%. Professional risk management requires correlation-adjusted position sizing and stress-testing portfolio scenarios.
A hard stop is a price level where you exit automatically, no exceptions. A soft stop is a level where you reassess — if your thesis is intact, you may hold; if it has changed, you exit. A time-based stop is an exit triggered by the passage of time without price movement (the trade is not working). All three are valid; which you use depends on the position type and market conditions.
Cash is a position. When you hold 30–40% cash, you are not "doing nothing" — you are positioned to exploit the dislocations that inevitably occur. The discipline is resisting the urge to deploy it slowly during non-dislocations. PTJ and Druckenmiller are famous for going to cash during periods of uncertainty and then deploying aggressively when their framework gives a high-conviction signal.
Every professional fund has circuit breakers — predetermined drawdown levels that trigger mandatory position reduction. A −5% drawdown triggers a 25% reduction. A −10% drawdown triggers a 50% reduction. A −15% drawdown triggers complete de-risking. These rules exist precisely because your judgment deteriorates as your P&L deteriorates — the rules must govern, not your emotions.
The Kelly Criterion calculates the mathematically optimal bet size: f = (bp − q) / b, where b = odds, p = probability of winning, q = probability of losing. Full Kelly is almost never used in practice (too volatile); most professionals use half-Kelly or quarter-Kelly. The formula requires knowing your edge, which most traders significantly overestimate.
We run simulations of 1,000 trades across different risk percentages (0.5%, 1%, 2%, 5%, 10%) with the same win rate and reward-to-risk ratio. The simulation makes visceral what the theory says: high risk per trade leads to ruin even with a positive edge. You will build your personal position sizing spreadsheet and never deviate from it.
Build a macro portfolio from scratch using correlation-adjusted position sizing. We use a simple correlation matrix to calculate effective portfolio risk, identify where concentration exists despite seeming diversification, and construct a framework for balancing conviction with correlation. Case study: a portfolio that looked diversified but was 90% correlated to "long risk" in March 2020.
No position should be entered without a written exit plan. The framework covers: the thesis (what must be true for the trade to work), the stop (what price or data point invalidates the thesis), the target (what price or data point confirms the thesis), and the re-entry criteria (if stopped out, under what conditions would you re-enter). We practise this with 10 historical trades.
The psychology of drawdowns and the mechanics of managing them. We study how Druckenmiller managed drawdowns at Quantum Fund — cutting size when wrong, pressing when right. We build a personal drawdown management protocol: the specific actions you will take at −3%, −5%, −8%, −10%, and −15% portfolio drawdown levels. The protocol must be written before you start trading, not during a drawdown.
How to think about cash as a strategic weapon. We study George Soros's approach to cash — he would sit in cash for months, then deploy aggressively when his framework gave a high-conviction signal. We discuss the psychological difficulty of holding cash when markets are rising and develop a systematic approach to cash deployment that is rule-based rather than emotion-based.
The capstone lesson. You will write a complete Risk Management Operating Manual — a personal document that governs every trading decision under stress. Sections: position sizing rules, stop loss framework, correlation limits, drawdown circuit breakers, dry powder management, event risk protocol, and the "what I will never do again" section. This document is updated after every major loss. It is your most important trading asset.
Build a Monte Carlo simulation in Excel or Python modelling 1,000 trades with: 40% win rate, 2.5:1 reward-to-risk ratio, and varying risk per trade (0.5%, 1%, 2%, 5%). Run 500 simulations for each risk level. Plot the distribution of outcomes. Calculate: probability of ruin (portfolio drops below 50%), median final portfolio value, and maximum drawdown experienced. Present the results as a table and write a one-page commentary on what this exercise taught you.
Take 10 historical trades (or 10 current paper trades) and write a complete exit plan for each: thesis, stop price, target price, and re-entry criteria. For the historical trades, compare your planned exits to the actual optimal exits. Where would your plan have worked? Where would it have failed? What rules would you add?
Write your first-draft Risk Management Operating Manual. It must include: your position sizing rules (with specific percentages), your stop loss framework (hard stops, soft stops, time stops), your correlation limits (maximum exposure to any single theme), your drawdown circuit breakers (specific actions at specific drawdown levels), and your dry powder management rule. Minimum 2 pages. Revisit and revise this after the Capstone project.
Study one of the following historical blow-ups in detail: Long-Term Capital Management (1998), Amaranth Advisors (2006), or the vol-seller blowups of February 2018. For each: what was the position? What was the risk per trade? What was the correlation problem? What was the circuit breaker that wasn't triggered? Write a 3-page case study and identify the specific rule from your Operating Manual that would have prevented the catastrophe.
Tie all six layers together in a live (or paper-traded) 30-day simulation managing a hypothetical $10 million macro portfolio. This is where theory becomes practice. You will experience the psychological reality of managing conflicting signals, sizing under uncertainty, and cutting losses on positions you believed in.
The framework is useless without the habit. Below is the exact daily and weekly ritual you should build until it becomes automatic.
| Time | Activity | What To Do |
|---|---|---|
| Morning — Before Markets Open (45 min) | ||
| 15 min | Global Market Scan | Review overnight: Asia close, European session, US futures. Check DXY, 10Y yield, gold, crude, VIX. What moved and why? |
| 10 min | Calendar Check | What events are on today? Any data releases? Central bank speeches? Adjust positioning expectations accordingly. |
| 10 min | Dashboard Glance | Quick check of your regime dashboard, liquidity composite, cross-asset divergences, and sentiment composite. Any signals flashing? |
| 10 min | Journal Entry | Write 3–5 sentences: What is the market's story today? What is your story? Where do they differ? |
| Evening — After Markets Close (20 min) | ||
| 10 min | Position Review | Check all positions against stops and targets. Calculate daily P&L. Note any positions approaching triggers. |
| 10 min | What Happened Today | What moved and why? Did the market's reaction to events match your expectations? Update your journal. |
| Weekend — Saturday or Sunday (2–3 hours) | ||
| 30 min | Dashboard Full Update | Update all indicators with latest weekly data: COT, AAII, fund flows, credit spreads, yield curve. |
| 45 min | Weekly Macro Note | Write the one-page weekly macro report: regime, liquidity, cross-asset signals, positioning, events ahead, highest-conviction idea. |
| 30 min | Portfolio Audit | Full position review: sizing, correlation check, gross/net exposure. Identify any drift or rule breaches. |
| 30 min | Deep Reading | Read one chapter from a macro investing book, one BIS/Fed research piece, or one long-form investor interview. |
| 15 min | Forecasting Calibration | Review last week's predictions. Were you right? Wrong? Update your mental model. Track your calibration score over time. |
Every trade must be justified by your regime assessment. If you do not know the regime, you do not trade.
You can be the best stock-picker in the world, but if liquidity is draining, your long positions will lose money. Do not fight the liquidity cycle.
When cross-asset signals disagree, one of them is wrong. Finding out which — and betting on the resolution — is where the edge lives.
Positioning and sentiment tell you who is already in the trade. At extremes, the crowd becomes your counter-party.
You cannot predict outcomes, but you can prepare for every scenario, size appropriately, and execute without emotion.
Alpha comes and goes. Strategies decay. Markets evolve. The ability to survive and compound is permanent.
A losing trade with good process is better than a winning trade with bad process. Over 1,000 trades, process determines results.
The cost of being wrong is small if you cut fast. The cost of being wrong is catastrophic if you hold and hope.
Having dry powder when others are forced to sell is the greatest advantage in macro investing. Guard it jealously.
If your thesis is not written, it is not a thesis. If your stops are not defined, they do not exist. If your rules are not documented, they will be abandoned under stress.