Stock Markets Analytics Zoomcamp 2026

Homework 4: Trading System Statistics

Distribution of scores and reported study time for this homework.

Submissions

48

Median total score

5

Average total score

6

Score distribution

All values are points.

Questions score

Min
1
Median
4.0
Max
10
Q1
4.0
Avg
5.6
Q3
7.0

Learning in public score

Min
-
Median
0.0
Max
3
Q1
0.0
Avg
0.4
Q3
0.3

Total score

Min
1
Median
5.0
Max
11
Q1
4.0
Avg
6.0
Q3
10.0

Time distribution

All values are hours reported by students.

Lectures

Min
1.0
Median
3.0
Max
48.0
Q1
2.0
Avg
6.1
Q3
3.8

Homework

Min
2.0
Median
3.0
Max
20.0
Q1
2.0
Avg
5.3
Q3
5.0

Question breakdown

Correctness and answer distribution per question.

1. Find the CAGR for the pred10_rf_best_rule_55 using the new setup (with fewer dummy variables)

19 / 48 correct (39.6%)

1 1.121 9 (18.8%)
2 1.126 19 (39.6%)
3 1.136 19 (39.6%)
4 1.141 1 (2.1%)

2. What is the probability threshold for the second-best Random Forest decision rule, if you want to have the number of average trades per day BELOW 5?

24 / 48 correct (50.0%)

1 0.54 0 (0.0%)
2 0.56 24 (50.0%)
3 0.57 19 (39.6%)
4 0.58 5 (10.4%)

3. What is the number of trades for the best predictor pred5_clf_10 (after redefining the target variable)

30 / 48 correct (62.5%)

1 6 0 (0.0%)
2 7 14 (29.2%)
3 8 30 (62.5%)
4 9 4 (8.3%)

4. [EXPLORATORY] Describe Your Ideal Trading Strategy

48 / 48 correct (100.0%)

Answer Count
My favorite trading strategy is to use Random Forest to predict stock returns with probability threshold of 0.56, I would make less than 5 trades a day, I would diversify and manage my risks, I would measure performance using CAGR and compare the results to a buy and hold strategy 2
Ensemble + Kelly sizing + risk controls 1
I would not put a flat $100 on every positive flag. Each morning I would take the reduced-dummy random forest, keep names with probability at least 0.57, rank them by probability, and invest only in the top three that day. That day's capital would be split equally across those names, so a quiet day does not leave cash idle and a crowded day does not force a long list of tiny bets. If fewer than two names clear 0.57, I would skip the day. I would treat agreement with the depth-10 tree from the 1.022 growth label as a soft filter: when both models agree I would allow a slightly larger size, and when they disagree I would skip. Round-trip fees stay at 0.2% in the simulation so the CAGR stays honest. 1
"My ideal strategy" (beyond $100-per-prediction): 1. Signal pipeline. Score all stocks daily with rf_best.predict_proba only; skip discrete 0/1 rules. Trade only when proba ≥ 0.60, sized by confidence: weight = (proba − 0.6) / 0.4 of the per-trade budget. 2. Trading budget & caps. Fixed daily risk budget (e.g. $2,000). Max 1% of capital per stock-day; max 3 concurrent bets per ticker; total open exposure ≤ 6 × daily budget (30-day horizon ⇒ ~1% of book per trade). 3. Trade frequency control. Instead of every marginal 1, keep at most the top-5 highest-probability names per day (cuts trades/day from ~6 to ~3 ⇒ fewer 0.20 round-trips and less chatter risk). Regime filter. No new positions when growth_snp500_30d < 1 and the tree's macro leaf says tightening (DGS10 > 4.5); occasionally one macro veto is worth more than ten stock signals. 5. Exit rules. Hold to 30 days unless: (a) stop at −8% from entry, (b) take profit at +15%, (c) opposite proba < 0.4 next day (model flip = early exit). 6. Reject & validate. Walk-forward on the TEST split only, compare CAGR/Sharpe vs always-in benchmark (invest $1,000 daily evenly, no signals). Kill the strategy if after ~0.05% slippage+fees and 1-day execution delay the net CAGR < benchmark, or if returns concentrate in <10 trading days. 1
My ideal strategy would combine multiple models and manage position sizing based on prediction confidence: 1. CONFIDENCE-WEIGHTED ALLOCATION: Instead of investing a flat $100 per positive prediction, I would use the prediction probability from the Random Forest to size positions. For example, invest $50 + $100 x (probability - 0.556) for predictions above the 0.56 threshold, so higher-confidence predictions get more capital. 2. MULTI-MODEL CONSENSUS: Only invest when at least 2 out of 3 models agree (e.g., Random Forest at threshold 0.57, Decision Tree at depth 10, and the manual DGS10 rule). This reduces false positives and trading frequency while maintaining precision. 3. SECTOR DIVERSIFICATION CAP: Limit maximum exposure to 3 stocks per day to reduce concentration risk, selecting the ones with the highest prediction probabilities. 4. DYNAMIC THRESHOLD ADJUSTMENT: Use a higher threshold (e.g., 0.60) during high-volatility periods (when VIX or ATR indicators are elevated) and a lower threshold (0.55) during calm markets, adapting risk to market conditions. This approach aims to improve risk-adjusted returns by being more selective and sizing positions based on conviction, rather than treating all positive predictions equally. 1
A reasonable strategy would trade only the strongest model probabilities, use equal or volatility-adjusted position sizes, cap exposure per ticker, and include transaction costs. I would validate the rules on a later test period and compare CAGR, drawdown, turnover, and Sharpe ratio with a buy-and-hold benchmark. 1
Confidence-weighted, concentrated and risk-limited. Signal. Use the Random Forest probability p = P(growth_30d > 1 | X) as the main signal. Only trade names with p >= 0.60: precision stays around 60% but the number of trades falls sharply compared with the 0.55 rule. Position sizing. Instead of investing $100 on every positive prediction, rank the daily candidates by p and allocate the daily budget to the top 3–5 names, with weights proportional to (p - 0.55) (or equal weights). Cap each position at 25–30% of the daily budget. Fewer trades / lower costs. Skip a name if it already has an open position from the previous 30 days, and avoid extreme-volatility or low-liquidity names. This reduces turnover and the 0.2% round-trip fees. Risk management. Keep 30–40% cash, limit to at most 2 names per ticker_type, and stop adding to a position after a 10% drawdown. Rebalance once a day at the close and hold for 30 calendar days. 1
I would rank each day's candidate stocks by the Random Forest's positive-class probability and invest only in the top three predictions above a 0.70 threshold. I would split a fixed daily risk budget equally across those positions, cap exposure to any one ticker, and keep unallocated cash when fewer than three candidates qualify. Positions would be exited after the 30-day forecast window, with a pre-set loss limit to control downside. I would compare this strategy against the notebook's $100-per-positive-prediction baseline on the same held-out test period, including CAGR, maximum drawdown, turnover, and fees. 1
A Random Forest model identifies the top three liquid assets with predicted positive-return probabilities above 65%. TradingAgent V3 then validates entries using 1H trend, 30M confirmation, and 15M entry signals, supported by EMA, RSI, ADX, ATR, volume, MACD, and Stochastic RSI. Risk management is central: maximum 1% capital risk per trade, 3% total portfolio exposure, structure-based stop-losses, and profit targets at 1.5R, 3R, and 4.5R. Positions are managed through partial profit-taking, trailing stops, and reassessment when opposite signals emerge. Models are reviewed daily, while intraday signals are evaluated after every closed 15-minute candle. The primary objective is consistent, risk-adjusted profitability through disciplined execution, capital preservation, and data-driven decision-making—not frequent trading. 1
My idea is not to buy on every positive prediction. I would trade only when two models agree: Random Forest with probability above 0.56 and the Decision Tree pred5_clf_10. So there are fewer trades, only the strongest signals, and less money goes to fees. I would also invest more when the model is more sure, for example $100 for probability 0.56–0.60 and $200 above 0.60. For risk, max 5 trades per day, trade only when the market is ok (for example 10Y yield below 4.5% or S&P 500 above 200-day average), and a stop loss around -10% so I don’t wait 30 days with a bad trade. 1
1. Every day I rank all stocks by the RF probability (proba_pred10). 2. I only consider names whose probability ≥ 0.60 (the efficient threshold found in Question 2). 3. I allocate the available daily capital equally across the top-3 ranked names (or fewer if less than three pass the filter). 4. Single-name exposure is capped at 5 % of total portfolio value. 5. I never hold more than 15 open positions; if a stronger signal appears I close the lowest-conviction existing position first. 1
I would use the reduced-feature Random Forest (208 features, no ticker or month-week dummies) as the primary signal. Each morning I rank names by predicted probability and keep those with p >= 0.57, the Q2 cutoff that keeps average daily investments below 5 while still delivering CAGR close to 1.12. I invest equally in the top 3 of that list, sizing each ticket as $100 times (p - 0.55) / 0.10 clipped to [0.5, 1.5], so a 0.65 name gets 1.5x a 0.55 name. I only take the trade if the depth-10 tree also predicts 1, because after raising the target to 2.2% future growth that tree had the best CAGR and its splits are macro filters (CPI, DGS10, FEDFUNDS). I cap the book at 5 simultaneous 30-day holds, exit at 30 trading days or on a 10% stop-loss, and skip new entries when S&P 500 30-day growth is below 1 and DGS10 is above 4. 1
The strategy i think of is for accumulation rather than highest earnings in a few trades, so this strategy focuses on investing most of the available cash including fees, evaluating composition every quarter to make decisions and prioritize acquisitions by maximizing number of acquired stocks from a pool of best available candidates focusing on macro and individual stock metrics. 1
I’d rank stocks by the Random Forest’s predicted probability and only consider trades above a threshold chosen using validation data. Each day, I’d buy up to the five highest-ranked stocks, allocating equal amounts while limiting each position to a small share of the portfolio and keeping some cash in reserve. I’d include fees in the backtest, use a predefined exit rule and stop-loss, and review performance with walk-forward testing before changing the strategy. 1
Each trading day, I rank all 33 tickers by predicted probability of 30-day growth above 2.2%, using an ensemble of the Decision Tree (clf_best) and Random Forest (rf_best). I only act on stocks whose ensemble probability exceeds a high threshold (≈0.65), and I take at most the top 5 ranked names per day — never all positives. I allocate **$200 per position** equally across those top-5 names (maximum $1,000 deployed per day, keeping the rest in cash), but I double the size (to $400) when the Random Forest’s rare high-conviction signal (`pred10_rf_best_rule_55`) also fires. 1
I would use the Random Forest trained on the reduced feature set (208 features), since it gave the best CAGR (1.128 at threshold 0.55). Each day I rank all stocks by predicted probability, keep only those above 0.57 and buy at most the top 3. This caps the load at 3 trades per day by design, instead of the 5-6 signals per day of the 0.55 rule. The portfolio is split into 30 equal daily slots, because positions are held for 30 trading days; each day's slot is divided equally among that day's picks, and an unused slot stays in an S&P 500 ETF. I skip a ticker that already has an open position, to avoid concentration in one stock. To manage risk, I close a position early at -8% (stop-loss) or +15% (take-profit). Finally, I would choose the threshold and the top-N on the validation period only, run the test period once, and compare the CAGR after fees with buy-and-hold S&P 500 over the same years. 1
As a long-term, buy-only investor, I would use the model to decide where my monthly contribution goes, not when to sell. Every month I invest a fixed amount: 70% goes into a fixed core of index ETFs (FTSE All-World, S&P 500, Nasdaq 100, DAX), and 30% goes into the 3 large US stocks that the Random Forest ranks highest for beating the All-World index over the next 6 months, but only if their probability is above 0.6. If the All-World index is more than 5% below its high, I invest double that month. I never sell, so trading costs stay very low, and I would compare the result with simply buying the All-World ETF every month. 1
Ideal Trading Strategy: High-Conviction Ensemble with Dynamic Sizing 1. Signal Selection & Ensemble: Instead of relying on a single rule, use an ensemble requiring consensus between the two best models: the Random Forest (rf_best) and Decision Tree (clf_10). Only generate a buy signal when both models predict positive growth and the Random Forest probability is ≥ 0.58 ≥0.58. 2. Trade Reduction (Top-K Filter): To eliminate over-trading and minimize round-trip transaction fees (0.2%), rank all daily positive candidates by their predicted probability and select only the top 2 to 3 highest-confidence stocks per day (instead of buying all qualifying stocks). 3. Position Sizing: Replace the rigid $100 fixed bet with probability-weighted position sizing: allocate a larger share of daily capital to predictions with higher model confidence ( ≥ 0.62 ≥0.62) and a smaller share to borderline predictions ( 0.58 − 0.61 0.58−0.61). 4. Risk Management: Cap maximum portfolio exposure to any single ticker at 10% of total capital. Enforce a hard 5% stop-loss rather than blindly holding for the full 30 days if the market turns downward. 1
My ideal strategy combines the Random-Forest probability with a daily capital budget and a concentration limit. Every trading day I rank all stocks by the RF probability (proba_pred10). I only consider stocks whose probability ≥ 0.60 (the efficient threshold found in Q2). I invest the available daily capital equally in the top-3 ranked names (or fewer if less than three pass the threshold). Position size is capped at 5 % of total portfolio value to control single-name risk. I never hold more than 15 open positions at once; if a new high-probability signal appears I close the lowest-conviction existing position first. This keeps average daily trades well below 5, concentrates capital on the highest-conviction signals, and adds simple risk controls that the pure “$100 per positive prediction” rule lacks. 1
My ideal strategy is a high-precision, low-frequency ensemble: (1) Signals come from a small mix of technically confirmed entries (CCI/MFI extremes co-occurring with bullish candlestick patterns) filtered by a friendly macro backdrop — gently rising DGS10, a non-inverted yield curve, no recession flag — and confirmed by short-term positive momentum in the benchmark (S&P growth > 1). (2) A Random Forest with a probability threshold high enough to hold average trades per day under ~5 (so the 0.2% round-trip fee stays a small fraction of gross return), targeting precision >= 55–60%. (3) Equal $100 positions on every signal over a fixed ~30-day horizon (no leverage), with capital sized from the 75th-percentile daily signal count so the account can absorb a full month of consecutive trades without margin. (4) Walk-forward re-tuning every quarter, hard stop-losses and a per-ticker cooldown after losing trades to avoid regime drift and overtrading. The goal is double-digit CAGR with single-digit drawdowns rather than maximum absolute return. 1
This strategy uses a Random Forest model to identify stocks likely to finish in the top third of the market over a 30-day period, rather than merely predicting absolute price gains. To adapt to new trends without data leakage, the model is retrained every six months using historical data with a strict 45-day buffer. Execution is strictly systematic: whenever a stock scores above a budget-adjusted threshold, you invest exactly $100 and hold it for 30 trading days with no stop-losses and a limit of one position per company. By targeting the market's relative winners rather than relying on overall market growth, this approach significantly outperforms a basic model, achieving a 15.1% Compound Annual Growth Rate (CAGR) compared to the baseline's 10.3% and generating $13.6k in profit versus $8.7k on the same $18k reserve. 1
I tried the following strategy , Two models must agree. Buy only when both the depth-10 tree (pred5) and the Random Forest at ≥ 0.56 say "buy". The logic was that the two models are built differently, so agreement should filter out more false signals than either one alone, giving fewer and better trades. When I tested this strategy with the help of Claude Code , the CAGR dropped down to 4.6% , so the idea does not work . 1
Use Random Forest to rank stocks each day, require a probability threshold selected on rolling validation periods, and take at most the three highest-ranked eligible signals. Invest at most 2% of current portfolio equity per new position. Skip a trade if exposure limits would be breached. 1
My ideal strategy combines model ensembling with risk-adjusted capital allocation. First, I use Random Forest with probability threshold >= 0.57 to maintain a selective trade frequency below 5 investments per day. Second, rather than equal-weight bets of $100 across all signals, I size positions dynamically using fractional Kelly criterion capped at the top 3-5 daily predictions with a 3% trailing stop-loss to limit downside and preserve convexity over the 30-day horizon. 1
Only buy when the random forest at 0.56 and pred5_clf_10 agree. Each day rank those stocks by the forest probability, keep the top 3 and split the day's budget equally between them, with at most one open position per ticker. Skip new entries while the 10-year yield is above 4.5%, since that rule carried signal in Module 3. Hold 30 days as now, but sell early on a 10% loss. That should bring it down to 2 or 3 trades a day and cut fees, and it can be checked on TEST the same way as sim1. 1
I would combine the two best models and trade less but with more conviction: Signal: buy only when both the Random Forest (setup without ticker dummies) gives probability ≥ 0.57 and the depth-10 Decision Tree trained on the stricter target (growth > 2.2%) predicts 1. Agreement between two different models filters out a lot of false positives. Selection: each day, rank the candidates by RF probability and take at most the top 3 (this caps the time spent and the capital needed). Sizing: split the available cash into 30 slots (one per day of the 30-day holding period) so money is always fully used and gains are reinvested; give 1.5x size to probabilities ≥ 0.62. Risk management: 8% stop-loss and 15% take-profit per position, no more than 2 open positions in the same stock or 40% of capital in one country/sector, and no new entries when the S&P 500 is below its 200-day moving average or VIX > 30. Evaluation: run an exact day-by-day simulation (iterating rows, with fees and reinvestment) and compare CAGR, Sharpe ratio and max drawdown against buy-and-hold of SPY over the same TEST period. 1
My ideal strategy uses an ensemble consensus filter combined with rank-based allocation and dynamic risk controls: 1. Signal Selection: Combine the Random Forest model (threshold $\ge 0.57$) with the Decision Tree (clf_10) and macro confirmation (Treasury yield slope / Fed funds rate). An asset is only bought if both ML models predict positive growth. 2. Turnover Reduction (Top-K Selection): On days with multiple positive signals, rank candidates by model probability and select only the top 3 highest-conviction trades to curtail transaction fees. 3. Position Sizing: Allocate capital using inverse-volatility weighting (risk parity) rather than fixed $100 bets, with a strict 15% maximum single-asset portfolio cap. 4. Risk Management & Exits: Implement a 4% trailing stop-loss to eliminate left-tail drawdowns before the 30-day horizon, coupled with a +8% take-profit exit rule. 1
Fewer high conviction trades. Add ranking to daily list and back-test effect based of that. Consider limiting open positions to 5 and cutting holding period to 5 days (time stop) → test/prove whether winners tend to move immediately (a common "street knowledge"). This should cut open exposure and value-at-risk to only the winners, and could further help move capital to better prospects. 1
I would use the Random Forest model to rank liquid stocks by their estimated probability of positive returns over the next 30 trading days. Each day, I would select no more than three high-confidence opportunities, subject to a minimum probability of 0.65 and restrictions on correlated positions. I would limit each investment to 2% of available capital, account for transaction costs and slippage, and impose portfolio-level drawdown and concentration limits. I would then assess the strategy using walk-forward testing, comparing net CAGR, maximum drawdown and average trades per day against a passive benchmark. 1
i would use an optimized Random Forest Classifier (depth 13) with 205 features to generate trading signals and dynamically allocate capital according to prediction confidence: $100 for probabilities between 55% and 60%, $200 between 60% and 65%, and $400 for probabilities of 65% or higher. The strategy limits activity to a maximum of eight active bets per day, applies a 3.5% trailing stop-loss and a 15% take-profit, and closes positions after a maximum holding period of 30 days. 1
My strategy combines model agreement, top-K selection, and risk-based position sizing. Each day, I score all stocks with the Random Forest probability (fewer-dummies setup from Q1). I only keep candidates with probability ≥ 0.56, the efficient threshold from Q2, where the Decision Tree (max_depth=7) also predicts growth. From these, I take at most the top 3 by probability per day. This cuts noisy, low-conviction trades, fees, and time spent. Since each position is held for 30 trading days, I invest a fixed daily budget of about 1/30 of capital instead of a flat $100 per signal. The budget is split across the selected stocks inversely to their recent volatility, capped at 10% of capital per ticker and 30% per ticker_type to avoid concentration. For risk management, I skip new entries when the market regime is weak (S&P 500 30-day growth < 1), use a stop-loss around −8% and a take-profit around +15% within the holding window, and allow only one open position per ticker. I would tune the threshold and K on the validation period only, to avoid look-ahead bias. I would then backtest on the test period with explicit cash and position tracking, comparing CAGR, max drawdown, Sharpe ratio, and trades per day against pred10_rf_best_rule_55. 1
An ideal trading strategy combines predictive machine learning models, multi-modal data analysis (technical and macroeconomic indicators), strict risk management (such as ATR-based stop-losses and position sizing), and transaction cost control to ensure stable risk-adjusted returns. 1
My ideal strategy uses the Random Forest with a high probability threshold (≥0.60) as the main filter, combined with the decision tree (pred5), and I only trade when both models agree. Instead of investing a fixed $100 per signal, I split the day's available capital equally among the 3–5 predictions with the highest probability, with a cap per company and per sector to limit concentration risk. I skip days with an unusually high number of signals, since that may indicate an abnormal market regime, and I use a stop-loss of around −8% to limit losses. This reduces the number of trades and the 0.2% fees, and should give a more stable CAGR. In pandas, I would sort by the predicted probability within each Date and take the top N. 1
My ideal trading strategy would combine predictions from the Random Forest and Decision Tree models to identify stocks with the strongest potential for positive returns. I would prioritize stocks where both models predict growth, using a Random Forest probability threshold of at least 0.56, which performed well in our simulations while reducing trading activity. I would select a maximum of 3–5 stocks per day, rank them by prediction confidence, and allocate my available investment capital equally among them. To manage risk, I would diversify across stocks, limit exposure to any single position, and use predefined stop-loss rules. I would also account for transaction fees and evaluate the strategy using CAGR, maximum drawdown, and risk-adjusted returns through backtesting before investing real money. 1
Combine the Random Forest at threshold ≥ 0.57 with the depth-10 Decision Tree (pred5_clf_10), and only invest when both predict growth. Each day, take at most the top 3 signals ranked by Random Forest probability, so trades stay under 5 per day. Split available cash equally among them instead of a flat $100, and keep about 20% in reserve. Skip a ticker that already has an open position, to avoid stacking risk. Set a stop-loss around −8% so one bad 30-day hold can’t wipe out the gains. 1
My ideal trading strategy would use `pred5_clf_10` because it provides a good balance between CAGR and the number of trades. I would invest only when the predicted probability is above 0.7 and select the top 3 opportunities each day. I would divide my available capital equally between selected stocks, limit the amount invested in each stock, and keep some cash in reserve. I would also account for transaction fees and use stop-loss limits to reduce potential losses. Finally, I would evaluate the strategy on historical data before using it in real trading. 1
I would use a high prediction threshold (above 0.7) to select only the most promising trades. I would invest a fixed percentage of my available capital in each selected trade, use stop-loss limits to minimize losses, and diversify across different stocks. I would also set daily loss limits and regularly evaluate model performance to improve returns while managing risk. 1
Each day, invest only in the top 3 stocks by Random Forest probability (≥ 0.60), and only if the decision tree also predicts growth. Split capital across the 30-day holding window, weight by confidence, and cap each position at 30%. Use a 10% stop-loss and skip new entries when the S&P 500's 30-day trend is negative. 1
I would use a Random Forest retrained annually on the previous five years, including only observations whose 30-session outcomes are already known. Stocks with predicted probabilities of at least 0.55 would become buy candidates. A fixed initial budget would fund at most 20 equally sized positions, one per ticker, with purchases limited by available cash. When candidates exceed available slots, I would prioritize lower recent volatility. Positions would close after 30 trading sessions, accounting for fees of 0.1% per side. I would evaluate this strategy against a fixed model and the same portfolio rules without an ML filter. 1
My ideal trading strategy would combine predictions from multiple machine learning models and select stocks with the highest predicted probability of positive returns. I would use a probability threshold of 0.65 or higher and select the top 3–5 stock predictions each day. I would distribute my available capital equally among these investments rather than investing a fixed amount in every prediction. I would also apply stop-loss limits, diversify investments across different stocks, and limit exposure to any single stock to reduce risk. Finally, I would evaluate the strategy using CAGR, transaction fees, average daily trades, and maximum drawdown to balance profitability and risk. 1
My ideal trading strategy would use a machine learning model to identify potential trading opportunities based on historical market data. I would prioritize a strategy with consistent returns, a reasonable CAGR, and a limited number of trades per day to minimize transaction costs. I would also use a probability threshold to filter out low-confidence predictions and only execute trades when the model indicates a sufficiently strong signal. In addition, I would apply proper risk management, including stop-loss limits, position sizing, and diversification, to reduce potential losses. Finally, I would evaluate the strategy using historical backtesting and out-of-sample testing before applying it to real market conditions. My goal would be to achieve consistent long-term growth while maintaining an acceptable level of risk. 1
I would rank stocks by the reduced-feature Random Forest's predicted probability of positive 30-day growth and buy at most the three highest-ranked stocks each day, provided their probabilities are at least 0.57. I would invest $100 per selected stock only when cash is available, cap total exposure to any one stock at 5% of portfolio value, and hold each purchase for 30 trading days. I would implement this with a daily ranking and a ledger of cash, open positions, and scheduled exits; I would choose the threshold on validation data and evaluate the complete strategy, including fees, drawdown, and cash constraints, on a later untouched test period. 1
Strategy Overview: To optimize our trading performance while minimizing risk and trade frequency, I propose an Ensemble Consensus Strategy with concentrated, volatility-adjusted position sizing: Consensus Prediction (Risk Filtering): Instead of using a single classifier, we take predictions from three distinct models: Random Forest, XGBoost, and LightGBM. We only trigger an investment if at least 2 out of the 3 models agree that a stock's probability of success is above 0.65. Concentrated Capital Sizing (Max 3 Trades/Day): On days with many positive predictions, we select a maximum of the top 3 most confident assets (highest predicted probability) and split our daily budget equally (33.3% each) among them. Unused daily budget remains in cash to protect capital. Volatility-Adjusted Allocation: To handle risk, we scale position sizes inversely with historical stock volatility. High-volatility tickers receive a smaller capital allocation compared to stable, high-conviction assets. Dynamic Exits: We apply a strict 5% trailing stop-loss to limit downside risk and a 15% profit-taking rule to lock in gains before the standard 30-day holding window expires. 1
My Ideal Trading Strategy: High-Confidence Ensemble with Macro Filtering and Dynamic Sizing 1. **Prediction Selection & Ensemble:** Instead of relying on a single model, I would combine predictions from the Random Forest (`rf_best`) and the tuned Decision Tree (`clf_best`). I will only consider taking a trade if the ensemble probability (average predicted probability of both models) is above **0.65**. 2. **Macro and Risk Filtering (Reducing Trades):** To manage systematic risk, I will apply a strict macro regime filter: do not enter any new long positions if 10-year Treasury yields (`DGS10`) are rising rapidly (e.g., weekly change > 5%) or if core inflation indicators suggest a highly restrictive Federal Funds rate environment that historically suppresses stock growth. 3. **Dynamic Position Sizing (Capital Allocation):** Instead of flat $100 bets, I would scale the investment size based on prediction confidence. For example, if the average probability is between 0.65 and 0.70, invest $100. If the average probability exceeds 0.75, scale the trade up to $250. To avoid over-exposure on any single day, I would limit maximum active daily trades to the **top 3 strongest signals**. 1
i would like strategy that has a positive trade prob equal or above 50%, with a win rate not exceeding 1:3 and not less than 1:2,the risk would be decided by the volatily of the instrument in the the day tool like RSI are useful in advancing the strategy.the startegy would use fundamental analysis and real time market events that would influence the market. 1

Calculated: 2 November 2026, 14:43