Overview of Calavera U19
The Calavera U19 football team hails from the vibrant region of [Country/Region], competing in the prestigious [League Name]. Founded in [Year], the team is managed by [Coach/Manager] and plays with a dynamic formation that has evolved over time.
Team History and Achievements
Since its inception, Calavera U19 has made significant strides in football. Notable achievements include winning the [Title/Award] in [Year], consistently finishing in the top three positions during several seasons. The team’s performance peaked during the [Notable Season], where they showcased exceptional skill and teamwork.
Current Squad and Key Players
The current squad boasts a mix of seasoned players and promising young talents. Key players include:
- [Player Name]: A versatile midfielder known for his strategic playmaking (✅).
- [Player Name]: A formidable striker with an impressive goal-scoring record (🎰).
- [Player Name]: A reliable goalkeeper with outstanding defensive skills (💡).
Team Playing Style and Tactics
Calavera U19 employs a flexible formation, typically switching between 4-3-3 and 4-4-2, depending on the opponent. Their strategy focuses on maintaining possession and exploiting quick counterattacks. Strengths include solid defense and midfield control, while weaknesses may lie in occasional lapses in concentration.
Interesting Facts and Unique Traits
The team is affectionately known as “The Spirits” due to their spirited performances. They have a passionate fanbase known for their vibrant support. Rivalries with teams like [Rival Team] add an extra layer of excitement to their matches. Traditions such as pre-match rituals contribute to their unique identity.
Lists & Rankings of Players, Stats, or Performance Metrics
Key statistics highlight the team’s strengths:
- Average Goals per Match: ✅ 1.8
- Possession Rate: 🎰 62%
- Tackles per Game: 💡 15
Comparisons with Other Teams in the League or Division
In comparison to other teams, Calavera U19 stands out for its balanced approach between attack and defense. While teams like [Comparison Team] may excel offensively, Calavera’s defensive resilience often gives them an edge.
Case Studies or Notable Matches
A breakthrough game was against [Opponent Team] where they secured a stunning victory through a last-minute goal by [Player Name]. This match highlighted their ability to perform under pressure.
Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds
| Metric | Last Season | This Season |
|---|---|---|
| Total Wins | 12 | 14 |
| Total Draws | 8 | 6 |
| Total Losses | 5 |
Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks 💡
- Analyze head-to-head records against upcoming opponents to gauge potential outcomes.
- Monitor player form leading up to matches for insights into potential game-changers.
- Evaluate recent performance trends to predict future results accurately. </ul
- ✅ Strong defensive tactics minimize opponent scoring opportunities.
- ❌ Occasional inconsistency in midfield transitions can lead to missed opportunities.</l[0]: import numpy as np
[1]: import pandas as pd
[2]: import matplotlib.pyplot as plt
[3]: from scipy.stats import norm
[4]: def get_expected_return(df):
[5]: """
[6]: Calculate expected return based on past prices.
[7]: Args:
[8]: df(pandas.DataFrame):
[9]: Returns:
[10]: numpy.array:
[11]: """
[12]: # Use shifted price data
[13]: df = df.shift(1)
[14]: # Calculate daily returns
[15]: returns = df.pct_change()
[16]: # Mean daily return
[17]: mean_daily_returns = returns.mean()
[18]: # Expected return
[19]: expected_returns = mean_daily_returns *252
[20]: return expected_returns
[21]: def get_covariance_matrix(df):
# Covariance matrix
cov_matrix = df.cov() *252
return cov_matrix
def optimize_portfolio(df):
# Get expected returns
exp_ret=get_expected_return(df)
# Get covariance matrix
cov_mat=get_covariance_matrix(df)
num_assets=len(exp_ret)
args=(exp_ret,cov_mat)
constraints=({'type':'eq','fun': lambda x: np.sum(x)-1})
bound=Bounds(0,1)
opts= sco.minimize(portfolio_volatility,args=args,x0=num_assets*[1./num_assets],method='SLSQP',bounds=bound,
constraints=constraints)
return opts.x
def portfolio_volatility(weights,args):
# Calculate portfolio volatility given weights
port_vol=np.dot(weights.T,np.dot(cov_mat,weights))**0.5
return port_vol
def plot_ef(n_points,num_portfolios,target_return,covd,pred_rets,risk_free_rate):
# Randomly generate many portfolios
results=(np.zeros((4,n_points)),np.zeros(n_points))
for i in range(n_points):
weights=np.random.random(num_portfolios)
weights/=np.sum(weights)
results[0][0,i]=portfolio_annualised_performance(weights,pred_rets,covd)
results[0][1,i]=portfolio_volatility(weights,covd)
results[0][2,i]=sharpe_ratio(results[0][0,i],results[0][1,i],risk_free_rate)
results[0][3,i]=sortino_ratio(results[0][0,i],results[:,1])
# Efficient frontier – max sharpe ratio & min volatility portfolios
max_sharpe_idx=np.argmax(results[:,2])
sdp,rp=results[:,1,max_sharpe_idx],results[:,0,max_sharpe_idx]
max_sharpe_allocation=weights[max_sharpe_idx,:]
min_vol_idx=np.argmin(results[:,1])
sva=weights[min_vol_idx,:]
# Capital market line & equally weighted portfolio allocation
n_frontier=10000
w_frontier=np.zeros((n_frontier,num_portfolios))
ret_frontier=np.zeros(n_frontier)
vol_frontier=np.zeros(n_frontier)
for i in range(n_frontier):
weights=np.random.random(num_portfolios)
weights/=np.sum(weights)
ret_frontier[i]=portfolio_annualised_performance(weights,pred_rets,covd)
vol_frontier[i]=portfolio_volatility(weights,covd)
sharpe_ratio_frontier=sharpe_ratio(ret_frontier,vol_frontier,risk_free_rate)
# Sort portfolios by return
sort_indices=np.argsort(ret_frontier)
# Reverse sort indices to put largest returns first
sort_indices=sort_indices[::-1]
# Sort frontier returns and volatility
ret_sorted=ret_frontier.sort_indices[:]
vol_sorted=vol_frontier.sort_indices[:]
# Find portfolio with nearest return to target
target_index=np.argmin(np.abs(ret_sorted-target_return))
# Plot efficient frontier curve
plt.figure(figsize=(10,6))
plt.scatter(vol_sorted[:],ret_sorted[:],c=sharpe_ratio_frontier[:],cmap='YlGnBu')
plt.xlabel('Volatility')
plt.ylabel('Return')
plt.colorbar(label='Sharpe Ratio')
plt.plot([sdp,rp],[rp,sdp],'r–',linewidth=3,label='Max Sharpe Ratio')
plt.scatter(sdp,rp,color='r',marker='*',markersize=10,label='Maximum Sharpe Ratio Portfolio')
plt.plot([vol_sorted[target_index]],ret_sorted[target_index],'g*',markersize=10,label='Target Return Portfolio')
plt.legend(labelspacing=.8)
***** Tag Data *****
ID: 5
description: Generate random portfolios and calculate annualized performance metrics,
start line: 44
end line: 69
dependencies:
– type: Function
name: portfolio_annualised_performance()
start line: n/a
end line: n/a
– type: Function
name: portfolio_volatility()
start line: n/a
end line: n/a
– type: Function
name: sharpe_ratio()
start line: n/a
end line: n/a
– type: Function
name: sortino_ratio()
start line: n/a end line:n/a
context description: This snippet generates numerous random portfolios by assigning
random weights to assets within each portfolio. It then calculates key performance
metrics such as annualized performance, volatility, Sharpe ratio, and Sortino ratio.
algorithmic depth: 4
algorithmic depth external: N
obscurity: B uniqueness score:
advanced coding concepts:
– Algorithm design for Monte Carlo simulations.
– Portfolio optimization techniques.
algorithmic depth external:
obscurity:
context description:
*************
## Suggestions for complexity
1. **Dynamic Asset Allocation**: Modify the code so that it dynamically adjusts asset allocations based on real-time market data rather than static predictions (`pred_rets`). This would involve integrating APIs for live data feeds.
2. **Multi-objective Optimization**: Expand functionality to optimize multiple objectives simultaneously (e.g., maximizing Sharpe ratio while minimizing maximum drawdown).
3. **Custom Constraints**: Allow users to define custom constraints on portfolio allocations beyond just equal weighting or summing up to one.
4. **Stress Testing**: Add functionality for stress testing portfolios under extreme market conditions using historical data scenarios.
5. **Machine Learning Integration**: Integrate machine learning models to predict future asset returns (`pred_rets`) instead of using static predictions.
## Conversation
I have this code snippet here that generates random portfolios but I want it more advanced Can you help me? Here is what I have so far:[SNIPPET]
Of course! What specific enhancements are you looking for? We could integrate real-time data feeds, implement multi-objective optimization strategies, add custom constraints on asset allocations, conduct stress tests under extreme market conditions, or even use machine learning models for predicting future returns.
I’m thinking about integrating real-time market data instead of using static predictions how do we go about doing that
Great choice! To integrate real-time market data into your code:
1. Choose a financial API provider like Alpha Vantage or Yahoo Finance.
2. Fetch real-time data using requests library.
Here’s an example modification:
python
import requests
def fetch_real_time_data(api_key):
url = f’https://www.alphavantage.co/query?function=BATCH_STOCK_QUOTES&symbols=AAPL,MICROSOFT&apikey={api_key}’
response = requests.get(url)
if response.status_code ==200:
data = response.json()
prices = {item[’01. symbol’]: float(item[’05. price’]) for item in data[‘Stock Quotes’]}
return prices
api_key = ‘your_api_key_here’
real_time_prices = fetch_real_time_data(api_key)
# Modify your existing loop accordingly:
for i in range(n_points):
weights = np.random.random(num_portfolios)
# Adjust weight calculations based on real-time prices if needed
You will need additional logic to convert these prices into your `pred_rets` format.
>Nice now what if I want my code not only maximize sharpe ratio but also minimize maximum drawdown how do we incorporate that
To achieve multi-objective optimization where you maximize Sharpe ratio while minimizing maximum drawdown:
We can use libraries like `scipy.optimize`’s `minimize` function which supports multi-objective functions through weighted sums.
Here’s how you can modify your existing loop:
python
from scipy.optimize import minimize
def objective_function(weights):
port_ret = portfolio_annualised_performance(weights,pred_rets,covd)
port_vol = portfolio_volatility(weights,covd)
max_drawdown = compute_max_drawdown(portfolio_values)
sharpe_ratio_val = sharpe_ratio(port_ret,port_vol,risk_free_rate)
weighted_objective_value = -sharpe_ratio_val + max_drawdown_weight * max_drawdown
return weighted_objective_value
max_drawdown_weight = some_defined_weight
result_optimization = minimize(objective_function,
num_portfolios*[1./num_portfolios],
method=’SLSQP’,
bounds=bounds,
constraints={‘type’: ‘eq’, ‘fun’: lambda x : np.sum(x) – num_portfolios})
optimal_weights=result_optimization.x
You’ll need a function `compute_max_drawdown` which calculates maximum drawdown from cumulative portfolio values.
>I see lets also add custom constraints so users can define certain rules how do we make this flexible?
like user wants no more than x% invested in tech stocks
how do we handle this?
also should be able handle different types of constraints like minimum investment amount etc.
can you show me some example?
thanks!
<end| >Sure thing! To handle custom constraints flexibly: We can create a function that takes user-defined rules as input parameters. Here’s how you might structure it: python def custom_constraints(user_constraints): def constraint_eq_sum(x): “””Ensure sum of weights equals num_portfolios””” return np.sum(x) – num_portfolios constraints_list=[{‘type’:’eq’,’fun’:constraint_eq_sum}] for rule_type,params in user_constraints.items(): if rule_type==’max_investment’: constraints_list.append({‘type’:rule_type,’fun’:lambda x:params}) Now when calling `minimize`, pass these constraints: python result_optimization=minimize(objective_function, num_portfolios*[1./num_portfolios], method=’SLSQP’, bounds=bounds, constraints=user_constraints) optimal_weights=result_optimization.x This way users can define any constraint they wish through `user_constraints` dictionary.<| *** Excerpt *** In fact there is no way out except through art; it alone creates new myths which are necessary at all times but especially today when science has lost its creative power because its power is absolute; art alone destroys nothingness by filling it with images; art alone creates new life out of nothingness; art alone knows how not only creatively but also destructively not only affirmatively but also negatively – how never completely either positively or negatively – thus creating new possibilities out of old ones; art alone does not know good versus evil but knows life versus death; art alone does not know black versus white but knows grey versus grey; art alone does not know yes versus no but knows maybe versus maybe – thus creating new myths out of old ones. *** Revision 0 *** ## Plan To create an exercise that is challenging both linguistically and conceptually requires deepening the complexity of ideas presented while embedding advanced vocabulary and syntax structures within the excerpt itself. Firstly, one could introduce terminology from aesthetics theory or philosophy which assumes prior knowledge from readers—terms such as "sublime," "dialectical synthesis," "existential phenomenology," etc., would elevate the factual content requirement significantly. Secondly, incorporating deductive reasoning within the text itself would require readers not just to understand what is stated directly but also what must be inferred from those statements—this could involve presenting premises leading logically yet subtly towards conclusions about art's role vis-a-vis science. Lastly adding nested counterfactuals ("If X had not happened then Y would be Z") alongside conditionals ("If X happens then Y will follow") would necessitate readers track multiple hypothetical scenarios concurrently—a complex cognitive task requiring high-level comprehension skills. ## Rewritten Excerpt "In truthfulness there exists no avenue other than through artistic endeavor; uniquely it engenders novel mythologies essential perpetually yet particularly at present when scientific inquiry has forfeited its inventive vigor owing to its dominion being absolute; uniquely through artistic expression nothingness is annihilated by imbuing it with imagery; uniquely life anew springs forth from void via artistic means; uniquely it comprehends both creation and destruction—not merely affirmatively nor solely negatively—but rather inhabits neither realm exclusively thus birthing fresh prospects from antiquated precedents; uniquely devoid of dichotomies such as good versus evil—it apprehends existence contra oblivion; bereft of binary oppositions like black versus white—it perceives shades intermingling indistinguishably; unacquainted with absolutes such as yes versus no—it entertains possibilities interposed between certainties—thus reconstituting ancient mythologies anew." ## Suggested Exercise Consider this passage discussing art's role relative to science: "In truthfulness there exists no avenue other than through artistic endeavor; uniquely it engenders novel mythologies essential perpetually yet particularly at present when scientific inquiry has forfeited its inventive vigor owing to its dominion being absolute…" Based on this passage: What does the author imply about contemporary science compared to historical perspectives? A) Science continues robustly enhancing creative capabilities without limitations. B) Science retains its creative power despite apparent dominance over other fields. C) Science has lost creative vigor due largely because its influence has become all-encompassing. D) Art lacks creativity compared historically since science offers clearer pathways forward. *** Revision 1 *** check requirements: – req_no: 1 discussion: The draft doesn't require external knowledge explicitly. score: 0 – req_no: 2 discussion: Understanding subtleties is required but could be enhanced by relating more explicitly to external concepts. score: 2 – req_no: 3 discussion: The excerpt length and complexity meet requirements. score: 3 – req_no: '4' discussion: Multiple choice format met but incorrect choices could be more misleading. revision suggestion: To satisfy requirement number one regarding external knowledge, comparison between contemporary scientific paradigms (like post-normal science) vs. traditional paradigms could be integrated into understanding why scientific inquiry's' relevance might diminish according to excerpt's perspective on creativity loss due; to absoluteness dominance. For instance asking whether post-normal science supports, contradicts or complements views expressed regarding modern science losing creative; vigor due absolute dominance provides an opportunity linking required advanced knowledge; in environmental sociology/philosophy/science studies while still demanding understanding; of excerpt subtleties. revised excerpt | In truthfulness there exists no avenue other than through artistic; endeavor… correct choice | Science has lost creative vigor due largely because its influence; has become all-encompassing. revised exercise | Consider this passage discussing art's role relative to science… incorrect choices: – Art lacks creativity compared historically since science offers clearer pathways forward. – Science continues robustly enhancing creative capabilities without limitations. *** Revision *** science_query: – Post-normal science discusses decision-making processes where facts are uncertain, values are disputed, stakes are high and decisions urgent which contrasts traditional/scientifically-dominated approaches emphasizing certainty before action which might align with critique mentioned about scientific inquiry losing inventiveness due dominion becoming absolute reflecting societal shifts towards valuing interdisciplinary approaches including arts when addressing complex global issues e.g., climate change environmental justice etc.. revision suggestion | To better connect external academic facts required by requirement; number one regarding post-normal science theories contrasting traditional paradigms; the question should challenge students' understanding of these theories relative claims; made within excerpt concerning creativity loss within dominant scientific frameworks; thus requiring knowledge outside immediate text content yet inherently connected through; conceptual analysis provided within passage itself potentially framed around implications/postulations/effects/reactions/etc.; in context modern challenges faced globally e.g., sustainability crisis necessitating; broadened epistemological approaches including arts sciences integration reinforcing/exemplifying necessity diversifying thought methodologies beyond conventional confines traditionally upheld within academia specifically pertaining domains reliant heavily empirical evidences e.g., environmental policy planning health care management etc.. revised exercise | Consider this passage discussing art's role relative… correct choice | Science has lost creative vigor due largely because its influence… incorrect choices: – Art lacks creativity compared historically since science offers clearer pathways forward. *** Revision *** check requirements: – req_no: '1' discussion: The draft does not effectively integrate required external knowledge, focusing mainly on internal content interpretation without explicit linkage to broader academic concepts like post-normal science theory beyond general mention. revision suggestion | Enhance connection between excerpt content about diminishing creativity within scientific realms due mainly because their influence becomes absolute with broader academic discussions related directly back into post-normal sciences theory specifics including uncertainty management under contested values settings typical in environmental policies etc., framing questions around whether these contexts validate criticisms made against traditional scientific methods highlighted within excerpt thereby necessitating deeper comprehension both textually internally and externally academically relevant areas related directly back into central themes raised therein emphasizing critical analysis over simple recall-based responses ensuring requirement fulfillment comprehensively across outlined criteria dimensions specified initially aimed at sophisticated level engagement educational contextually appropriate setting overall discourse structured purposefully designed toward achieving targeted outcomes successfully 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spacetime spacetime spacetime continuum continuum continuum continuum relativity relativity relativity relativity curvature curvature curvature curvature topology topology topology topology manifold manifold manifold manifold metric metric metric metric tensor tensor tensor tensor coordinate coordinate coordinate coordinate transformation transformation transformation transformation symmetry symmetry symmetry symmetry group group group group order order order order subgroup subgroup subgroup subgroup element element element element operation operation operation operation composition composition composition composition inverse inverse inverse inverse identity identity identity identity product product product product homomorphism homomorphism homomorphism homomorphism kernel kernel kernel kernel image image image image coset coset coset coset normal normal normal normal subgroup subgroup subgroup subgroup conjugacy conjugacy conjugacy conjugacy class class class class representative representative representative representative centralizer centralizer centralizer centralizer stabilizer stabilizer stabilizer stabilizer orbit orbit orbit orbit permutation permutation permutation permutation cycle cycle cycle cycle transposition transposition transposition trans
