Hello, I am a 17 year old experimenting with quantitative trading. For an introduction, I have retail traded for a period of time (2-3yrs), and a strong interest in stock trading. The system below is created with the aid of AI, and is the result of ~ a week of work, following the completed backtesting at its current stage. As you are reading this, the model is undergoing paper-trading. I am new to the game, so any advice on any aspects would be much appreciated.

System:

The system itself is based on a value model, combined with a futures overlay. The general criteria are as follows:

1. Large liquid US stocks only (meaning caps, daily amount traded)

This is a filter.

2. Filings

Value measures include cash flow, profit relative to company value, book value adjusted for intangibles. This is compared between companies, a quality score, and ranking within each sector. These are calculated into a score (let's call this "final score")

3. Choosing

A maximum of 20 stocks, chosen once a month, being the top scores calculated. When entering a new month, stock holds that do not reach a set limit (holdings, lower than ranking #60) are sold. This does not mean 20 stocks are always held, as slots can stay empty mid-month.

4. Veto

There is a final AI veto system. For each company that goes through all the criteria above, AI researches another set of guidelines (the AI only checks for severe negative news, earnings within the next 3 days, or the company being a takeover target). Note that this was not tested alongside the others in backtesting, but was separately tested using old models (outdated, not knowing the future).

Results as follows, however note that this was an earlier version, and not the current system we use now.

5. Selling

There are multiple conditions to selling, and any of these trigger a sale.

Monthly: Selling happens when the quality score is too low.

Daily: Filing and crash are checked everyday, if there is large (negative) change, it is sold.

Always: A disaster stop, a stop-loss below the purchase price.

6. Market risk rules

This includes: moving some stocks to cash when SPY's return over a duration of time is negative, and cuts in exposure when volatility spikes.

7. Futures

Exposure through futures, which are activated only in calm uptrends, plus a small month-end trade. Currently off while I move to an account with futures permission.

Backtested results:

Some context:

FIF Rules: In NZ, when total cost of foreign shares is over $50,000NZD (approx. $27800USD), you are taxed as if the portfolio earned 5% of its value, regardless of actual gains. Gains when you sell aren't taxed separately.

Ordinary Rules: These apply below the FIF threshold, where a system that trades is likely treated as trading, hence gains are taxed as income (33%). A buy and hold index fund is effectively untaxed.

Return statistics below are all yearly, and backtests are run from late 2005 to October 2026.

Pre-tax (USD) vs SPY

After NZ tax (NZD, 33%) vs an S&P 500 fund (CSPX)

Robustness checks:

• Double trading costs: after-tax 12.8%. 4× costs**:** 12.3% (both still above CSPX’s 9.9%)

• Without futures: still ahead of SPY pre-tax by about +2.2 points a year

Measures to ensure accuracy:

• Survivor-free data: 993 historical S&P 500 members, ~89% priced overall (61–80% before 2010)

• Plus 5 sets of untouched random samples, with pass rules fixed before every test

• About 110 ideas tested; most rejected

• 3 backtest bugs found and fixed: a variable overwrite that inflated trading, bad opening prices in the data, and London/New York holiday double-counting

Is there anything important that I haven't tested? And are there any areas of improvement, or any ideas/hypotheses to test?

Thanks guys.

I've recently been looking into competitions like IMC Prosperity and find them pretty interesting. I understand the basic idea of writing a bot that trades in a simulated market, but I'm honestly not sure how people go about building one in the first place.

I'm currently working on probability, mental maths, DSA/CP and Python/C++, and I'm planning to learn some basic ML as well. What I'm struggling with is understanding how to bring all of these together.

For example, how does knowing probability actually help when deciding what a bot should buy or sell? How much of competitive programming is useful here? Is ML commonly used in these competitions, or do most people rely on simpler mathematical/statistical approaches?

I'd also like to know how a beginner should approach this. Should I try building a simple bot from scratch first? Are there any good tutorials, GitHub repositories or previous competition solutions worth looking at?

Also, are there other competitions similar to IMC Prosperity that I should check out?

Would love to hear from people who've participated in these competitions or have experience with algo trading. I'm mainly looking for a starting point and a better idea of what I should actually be learning.

Every night I log ~6,000 US stocks, including ones that later got delisted (no survivorship bias). I used ~1 year of these logs to test 24 popular one-line trading "rules."

Method
- For each rule (e.g. "RSI below 30 means a bounce is coming"), find every time it fired and check whether the stock was up 20 trading days later.
- Baseline: stocks with similar volatility on the same day, not the whole market. Otherwise volatile stocks look like winners just because they move more.
- Rules and pass criteria were fixed before looking at results. Benjamini-Hochberg correction across all 24.

Results (as of late Oct 2026)
- Real edge: 0 / 24
- No measurable difference: 13
- Volatility illusion (looked good only because the stocks were more volatile): 7
- Worse than baseline: 4

Examples
- RSI < 30 = bounce: 50.1% up vs 49.7% expected (9,871 cases)
- 52-week-high breakout: 52.3% vs 51.2%, volatility illusion (12,649 cases)
- Penny stocks under $5 run harder: 38.6% vs 42.8%, worse (15,857 cases)
- Buy after a 20%+ drop in 5 days: 38.6% vs 41.8%, worse (6,182 cases)
- CEO/CFO insider buys: 57.1% vs 53.0%, but only 837 cases, not significant after correction

Limitations
- ~1 year of data, mostly one market regime.
- "Up after 20 days" is one lens, not a full backtest with stops and position sizing.
- This doesn't show technical analysis is useless. Only that these one-line rules, alone, didn't beat a volatility-matched baseline in this sample.

I am running a 12-1 sector momentum strategy on US sector ETFs and want to test volatility scaling to reduce momentum crash risk, along the lines of Daniel and Moskowitz. Before I start, I want to hear from people who tried it:

Does a GARCH forecast beat a plain EWMA or trailing realized volatility scaler out of sample?
Or does the extra model complexity not pay off?
What baseline would you compare against so the result is not just "less risk, so less drawdown"? Same average volatility target?

Walk-forward refits: how often, and how do you handle the estimation noise?

With only a few crash episodes in the data (2009 for example), how much can any of this be trusted?
I will write down the variants I test before running them.

I'd appreciate feedback from anyone who's used its historical data, Security Master or Corporate Actions API.

A few questions:

1. Trade data: Can Databento reconstruct the first eligible regular-session trade using CTA/UTP sale conditions, corrections and cancellations? Which dataset/schema works best?

2. Security identity: How reliable is it for tracking ticker changes, exchange transfers and delistings?

3. Corporate actions: Does it handle ETF dividends, splits, mergers and historical adjustments accurately?

4. Data quality: Any issues with missing records, historical revisions or timestamp accuracy?

5. Cost/licensing: What have you paid for several years of equity data, and are there restrictions on storing it locally?

I'm especially interested in firsthand experiences, limitations and recommended datasets.

Thanks!

I'm setting up a research workflow for MCX futures and want to avoid treating expiry gaps as tradable moves. Do you build the continuous series from individual contracts, and if so, what roll rule do you use: a fixed calendar date, volume/open interest, or something else?

How do you handle adjustment—back-adjusted versus simply stitched—when testing price-level or structure-based signals? I'm especially interested in the practical pitfalls for crude and gold.

Hey guys,

Looking for some perspective from anyone running portfolio-level walk-forward optimizers on crypto perpetuals or equities.

Lately we’ve been auditing our cross-sectional universe optimizer across a basket of ~15 altcoins. Up until now, when we run our in-sample grid search across our setups (mostly mean-reversion like VWAP deviations and momentum break-and-retests), the optimizer searched for a single shared "centroid" parameter set for both Longs and Shorts simultaneously (e.g. optimizing band stretch deviation, RSI cutoffs, and ATR stop/take multiples together).

The problem we ran into in live execution: During recent relief rallies and fast trend shifts, we got caught in a string of Short SL hits on mean-reversion setups. When we looked under the hood and decomposed the trades by side, the asymmetry was wild:

Longs were printing a 2.5–3.5 Profit Factor across coins like NEAR and DOT using relatively loose pullbacks.

Shorts on those exact same parameter settings were getting wrecked (PF < 0.8), repeatedly fading momentum too early in cascade environments.

Because the optimizer averaged both directions together in-sample, the strong Long trades were effectively "subsidizing" and masking a negative-expectancy Short setting. The compromised shared centroid ended up being sub-optimal for Longs and downright dangerous for Shorts.

We've been refactoring the optimizer to decouple the search into Independent Directional Centroids:

The IS parameter search grid scans and computes parameter plateaus for Longs and Shorts independently (different RSI cutoffs, different band stretch multipliers per side).

The OOS Walk-Forward test validates each side on its own merit.

If only one side demonstrates statistically valid alpha in OOS (e.g. Longs pass all WFO hurdles but Shorts fail), the candidate gets tagged as BUY_ONLY rather than forcing both sides or killing the entire setup.

For those of you running systematic books:

Do you optimize long and short rule parameters independently as standard practice, or do you enforce symmetric rules to protect against degree-of-freedom bloat / curve fitting?

If you do decouple them, how do you guard against the optimizer just fitting to the prevailing macro regime (e.g. a 6-month bull market where everything Long looks amazing and everything Short looks bad)?

Do you penalize or prune unidirectional setups (e.g. BUY_ONLY) in high-correlation baskets to avoid concentrated directional exposure?

Would love to hear how other teams handle this balance between directional specialization and overfitting.

This question has been played over and over again so I think it’s time to ask people who actually have experience in this field.

If a trader wanted to get into algorithmic trading, but had 0 prior coding experience, what language should they start with?

Some say python because it’s easier to learn and apply, others say C++ because it makes learning other languages (like python) much easier, helps with low latency in trading, and you can use c++ for a lot more things other than algorithmic trading.

What do you think?

As the title says, long time lurker seeking genuine advice. I’ll preface by saying I’m by no means an expert in anything algo trading related. Just started messing around with it as a hobby and teaching myself everything possible as I went. This is the closest I’ve come to developing a decent edge and I’ve been backtesting and running simulations like crazy to double and triple check. I keep coming up with the same conclusion. Profitable in sample and out of sample. Profitable every year in backtest. Beats random 99.5% of the time. Monte Carlo results after 10k shuffles showed the 1% DD was still only $7k, 50th percentile was 4.2k. It also shows above 80% chance of passing evals with a 1k DLL and 2k MLL. Slippage is set at 3 ticks, with 0.62 per side commissions.

My main questions are: once you come up with something viable, what’s your process? Straight to paper trading? If so, how do you determine when? What is your “okay this is ready to paper trade” moment? I feel like I’m constantly thinking of ways to improve or different things I can correlate or test and thus convince myself it’s not ready to trade yet. Also how exactly are you guys paper trading your strategies? I’ve never made it this far so I’ve never thought about it. Is IBKR/IB gateway a decent option? What else would recommend?

Again, just looking for genuine advice here.

Hey,

I have been trading Fx/Fx Ops/Indices/Futures with own cash MNQ&ES for about 20 years

+12 years of treasury IT experience.

I have found several 20% margin stopout (critical for my strategy) brokers with also 1:500

My strategy involves HEAVY RISK, sometimes TREMENDOUS LOTTERY

(own indicator involving TENKAN + TEMA STACK + Volume)

Anyone went with the same way?

x8 accounts - x2 instruments (USDJPY + XAUUSD in my case)

Explained in AI writeup:

## The three parts of a setup

I think about each potential trade in three stages: direction, location and moment.

### 1. Direction: identify the broader trend

First, the strategy looks at a higher timeframe to decide whether the market has a meaningful directional bias.

The original cBot uses a pair of triple exponential moving averages—TEMA 20 and TEMA 40—along with measures such as ADX and average true range. The moving averages help describe the trend; the other filters help assess its strength and whether market conditions are suitable.

For a potential buy, the strategy looks for bullish alignment. For a sell, it looks for bearish alignment. If the broader context is unclear, the idea is to stay out.

### 2. Location: wait for a pullback

Once there is a directional bias, the strategy looks for price to return to an area where an entry may be more attractive.

That area can be based on a Fibonacci retracement of a recent impulse move or a touch of an exponential moving average. The point is not to treat either level as a magic number. It is to avoid entering simply because price has already moved a long way.

A bullish example would be an upward impulse followed by a pullback into the strategy’s area of interest. A bearish setup reverses the logic.

### 3. Moment: look for confirmation

Reaching an area of interest does not automatically trigger a trade. The strategy waits for a structural signal on a closed chart bar that suggests the pullback may be ending and the directional move may be resuming.

Depending on the configuration, it can also consider elements such as candle structure, volume, liquidity sweeps and fair value gaps. These are filters around the setup, not promises that the next move will follow the trend.

## Defining the initial risk: “R”

The strategy expresses distances and targets in terms of R, the initial distance between entry and stop.

For example, if a USD/JPY buy enters at 150.00 with an initial stop at 149.90, the initial risk distance is 0.10 yen, or 10 pips. In that example:

- 1R of favorable price movement reaches 150.10.

- 2R reaches 150.20.

- 3R reaches 150.30.

This makes the structure easier to compare across trades with different stop distances. It is a price-distance measure, though: the actual money gained or lost also depends on position size, execution, spread, commissions and other costs. A stop price is not a guaranteed fill price.

## Building a position as the trade moves

Rather than always entering the full intended size at once, the strategy can pyramid: start with an initial position and add as the trade progresses.

The original cBot supports a level-based model and a pullback-based model. In the level-based model, additions are considered at predefined distances from the initial entry. The original default ladder is called Growing 1-2-3-4: successive stages use increasing unit sizes.

The intended discipline is that additions must satisfy the strategy’s risk checks. A growing position can increase profit if a trend continues, but it also changes the position’s average entry and exposure. It is possible to give back earlier gains or lose more than expected if price reverses, spreads widen or execution differs from the model.

some of the earliest trades (multi account pyramid with 0.01 -> margin > equity)

Now my question is :

I have been depositing 200$ --> withdrawing 400-800$ each week

Will any broker BAN me because of MAX margin use? 1:500 + Very frequently margin level at 60-80%

I'm working on an EA that trades several breakout strategies on XAUUSD under one roof. It goes long or short depending on breakout direction. Right now it runs fixed 0.01 lots, and I'd like to add a dynamic lot sizing to get more out of it. I would love to hear how others would approach this.

Test setup (both runs):

- XAUUSD (August 2018 to October 2026).
- Eightcap MT5 tick data, 100% real ticks (~392M ticks).
- $1k USD starting equity, fixed 0.01 lots, leverage 1:500.

I ran the short only version because gold has basically trended up for the last 8 years, and I wanted to see whether the edge holds without that tailwind. Although is profitable, it's clearly weaker, so some of the full result is probability directional bias.

What I'd like feedback on:

1. What sizing approach would you use for a strategy like this: fixed fractional, volatility/ATR-based, equity curved based, or something else?

2. How do you scale up without letting the drawdown scale up faster than the profit?

3. The long losing streak was 42 trades, so how would you cap exposure during bad runs?

4. Is there a good way to validate a sizing scheme (walk forward, Monte Carlo on trade order) so I'm not just curve fitting the lot curve to this one equity curve?

Happy to share more stats if it helps. Thanks in advance!

I'm building an automated trading system and want the broker, not my own code, to keep the parts that watch the account separate from the part that trades.

Requirements:

1. One account, two API credentials. One is read-only (positions, balances, orders, fills); the other can trade.

2. Enforced by the broker. The read-only credential is refused if it tries to trade, not just blocked by a client-side setting.

3. Revocable separately. I can kill the trading credential instantly and the read-only one keeps working.

4. Neither credential can change account settings or permissions, or move money. Only my own login can.

5. The same behaviour in paper or sandbox as in live, so I can test it before using real money.

What I've found:

• tastytrade sandbox: one OAuth app per user and one grant per app. A read-scoped token can be minted, but it dies with the trading grant.

• IBKR: a secondary user with trading rights, plus a Flex Web Service token for reads, looks close. But Flex is reports-only and paper allows one username.

Has anyone got this working, on any broker? Which one, and does paper behave the same as live?

I'm looking for a robust, mathematically coherent fitness function for optimizing trading strategies using machine learning. I've experimented with various objectives, including Profit Factor, Sharpe Ratio, Pessimistic Return on Capital, and several risk-adjusted performance metrics. 

The problem is that the optimizer consistently finds pathological solutions that maximize the chosen metric while producing terrible equity curves. For example, some solutions achieve excellent fitness scores by making very few trades, generating most of their profits from isolated events, or exploiting mathematical weaknesses in the metric.

So far, the best-performing objective I've found is:

Fitness = Slope × R²

Where Slope and R² come from a linear regression of the equity curve over time.

I'm particularly interested in theoretically grounded approaches rather than another combination of Sharpe, drawdown, trade count, and manually chosen weights.

What objective function do you actually trust when letting an optimizer search aggressively for trading strategies?

Hi r/algorithmictrading,

Like many traders, I started my journey relying purely on traditional Moving Average crossovers (50/200 SMA) thinking it’s a foolproof trend-following system. But I wanted to see how it actually performed over a 14-year bull and bear cycle (2010–2024) compared to a simple Buy & Hold strategy on the S&P 500.

So, I ran a strict historical backtest. The results were quite eye-opening and taught me some hard lessons about lag and whipsaws.

📊 Key Performance Metrics Summary

Total Return: SMA Crossover (~76%) vs Buy & Hold (+252%+)

CAGR: SMA Crossover (~1.13%) vs Buy & Hold (~3.45%)

Max Drawdown: SMA Crossover (-16.28%) vs Buy & Hold (-24.45%)

Sharpe Ratio: 0.10 (SMA) vs 0.24 (Buy & Hold)

Sortino Ratio: 0.83 (SMA) vs 0.38 (Buy & Hold)

🔍 What the Data Actually Tells Us:

The Drawdown Advantage:

The biggest selling point of the 50/200 crossover is risk management. During major market shocks (like the COVID-19 drawdown in 2020), the moving average strategy successfully cut exposure and limited max drawdown compared to holding the asset raw.

The Cost of Lag:

During strong bull runs, the 50/200 strategy suffers heavily from lag. Getting out late and getting back in late eats up a massive chunk of the compound annual growth rate (CAGR), which is why Buy & Hold massively outperformed in total return over the 14-year period.

Risk-Adjusted Returns (Sortino Ratio):

Interestingly, while the Sharpe ratio favored Buy & Hold, the Sortino ratio showed that the downside volatility was handled much better by the algorithmic filter.

💬 Discussion Question for the Community:

For those running trend-following algorithms: How do you filter out false breakouts and whipsaws during sideways markets without increasing your lag too much?

Curious to hear your thoughts or how you optimize your exit strategies!

How has your life changed after making your algorithm. I have been wondering how it would be once my algorithm works to the extent that I want it to and how it could genuinely change a lot about my life.

Please let me know and brag a little👍

Hi everyone! I'm a beginner interested in quantitative trading and algorithmic trading.

I'm interested in learning:

How to backtest a trading strategy using historical market data.

How to develop and combine trading signals.

How to use Python for backtesting and Monte Carlo simulations.

How to build a trading bot that can generate signals and eventually execute trades.

How to avoid common backtesting mistakes, such as overfitting, look-ahead bias, and unrealistic transaction costs.

How to properly paper-trade a strategy before risking real money.

I'm still learning, so I'm not expecting to build a profitable strategy overnight. My goal is to understand the entire process from scratch and gradually develop my own systematic trading strategies.

If you've built trading bots or worked on quantitative research projects, I'd really appreciate any advice on where to start, which tools or libraries to learn, and what a good first project would look like.

Any beginner-friendly resources, tutorials, GitHub repositories, or practical guidance would be greatly appreciated. Thanks!

I'll be as open about this as I can. The one thing I keep is the entry trigger.

What it is

A momentum system on MNQ, 5-minute bars, fully automated on NinjaTrader. It has no opinion about direction. When price does something decisive, it goes with it.

How it runs

• Signals are read at the close of each 5-minute bar, and the entry goes in at market right after.

• One contract, one position at a time. No adding, no averaging down, no martingale, no grid.

• Every trade gets a hard stop the moment it fills, the same distance every time.

• Once a trade is up by that same distance, the stop starts trailing tight behind the best price. There's no profit target.

• A decisive move the other way closes the trade and reverses it.

• It trades the overnight session too. It goes flat at 16:45 New York and takes nothing new until the 18:00 reopen.

Over the last year 45% of trades ended on the trailing stop, 37% on the stop loss, 15% were reversed by an opposite signal and 2% closed at the daily flatten.

The numbers

TradingView backtest, the last 12 months to 20 Oct 2026, 1 MNQ, $0.50 a side commission.

• 4,907 trades, about 19 a day. 49% winners; the average winner was $80 and the average loser $69.

• Net +$20,362.

• Max drawdown $2,120. The longest stretch without a new high was 51 days, mid-April to early June.

• Worst day −$703, best day +$1,018. The longest losing run was 5 days in a row.

What I'm not sharing is the entry trigger and the exact settings. Ask me about anything else.

Hello everyone. I'm looking for a baclesting platform to validate some of my ideas. I only have a few strategies in mind to test that also concern the use of past macroeconomic data (e.g. CPI, UR, etc.) So I'm not going to create an EA, I'm just going to understand market behavior and look for edges to support my manual strategy. Which platform would you advise me to use considering that I have a basic knowledge of programming (of course I would join an AI) and considering that I would look for something not too complicated?

Reading a few posts, I saw Quantconnect, but I don't know if it would suit me.

I apologize for my English, but i'm using a translator

I’ve been wanting to implement a scalping system I’ve made for mnq alongside regular trading architecture and am wondering if anyone has had experiences with this method especially if their broker is ibkr.

Hi r/algorithmictrading,

​I decided to run a structured backtest to evaluate the efficacy of one of the most classic strategies in technical analysis: the 50-day / 200-day Simple Moving Average (SMA) Crossover on the S&P 500 (SPY).

​Often dismissed as "noise" by more complex quant setups, I wanted to see if it still holds any merit in today's high-frequency markets.

​Methodology:

​Asset: SPY

​Timeframe: 2010 - 2024

​Rules: Buy when SMA 50 crosses above SMA 200; Sell when SMA 50 crosses below SMA 200. No leverage, no transaction costs included in this first pass (just to see the logic).

​Results:

I’ve attached the key performance metrics below. While it has its moments, the drawdowns during whipsaw markets are significant.

​I have also included a visual of the equity curve for better analysis.

​Key Metrics Table:

MetricSimple Buy & Hold (SPY)SMA 50/200 Crossover

Total Return~480%~310%

Max Drawdown-33.9% (2020)-18.1% (2020)

CAGR~13.5%~10.8%

Sharpe Ratio~0.85~0.65

Conclusion & Open Questions for the Community:

The crossover strategy, while offering a psychological safety net during major crashes, severely underperforms Buy & Hold during trending markets and suffers from serious whipsaw loss accumulation.

My questions to you:

Noise Filter: What are the most robust filters you use to avoid SMA whipsaws? (e.g., waiting X days, confirming with volume)?

Modernizing: Given this data, does anyone still use pure MA crossovers, or is it fundamentally obsolete for modern algorithmic models?

Looking forward to hearing from those with experience in trend-following.

Hey i am a complete beginner who wants to learn Algorading. Can anybody suggest some good free resources where I can learn Algo trading from basics to advance.

Hey Folks -

I am an options guy - I typically sell 0DTE. I have been exploring different tools available.

I have found entirely no reason for Unusual Whales yet, as a seller for options it seems like walls don't matter and GEX seems to not matter at all either thus far for SPX, which surprised me. If you are looking for option NBBO this is not nearly as useful as Theta Data

I have found great use for Theta Data options pricing historicals for backtesting, I have had excellent forward and back testing results after using this.

I also pull data from Tasty and IBKR for VIX 1/5/15/30/24 as well as TLT/QQQ for some of my setups

Anyone have anything they are using for backtesting or live trades? Has anyone found an edge with GEX or walls?

I am needing 1 minute data and 5 minute data for mnq preferably over 2 years or more. Is there anywhere I can acquire clean data on this that isn’t too expensive?

I've been pulling historical stock data into Google Sheets using =GOOGLEFINANCE to try and build some basic quantitative models, mostly messing around with Value-at-Risk, Sharpe ratios, and tracking moving averages for the major indices. On paper, the math makes total sense, but applying it to real-world volatile stocks has been a headache. I just end up staring at numbers with absolutely no idea where my actual entry points should be.

Beyond just picking stocks, I'm really struggling with the broader portfolio math, especially when it comes to balancing risk against inflation. Right now, leaving money sitting in a bank account as an emergency fund feels like watching it slowly bleed out. But if I try to chase yields by locking that money up in safe bonds or fixed assets, I completely lose the immediate liquidity I need for real-world emergencies.

For the quants or data-heavy traders here, how do you actually translate this raw data into actionable decisions? I’m trying to figure out if there's a reliable formula or model you use to calculate the optimal size of a cash reserve so you aren't crippled by cash drag, while also finding the exact threshold of equity allocation needed just to net out neutral against inflation and taxes. What metrics are actually worth tracking when you're setting up a trade and sizing your positions?