MindMap Gallery Quantitative investment mind map
Including concepts, models, and investment portfolios, quantitative investment is an investment strategy that uses quantitative methods and computer programming to issue buying and selling orders with the purpose of obtaining stable returns.
Edited at 2023-12-02 14:52:53This is a mind map about bacteria, and its main contents include: overview, morphology, types, structure, reproduction, distribution, application, and expansion. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about plant asexual reproduction, and its main contents include: concept, spore reproduction, vegetative reproduction, tissue culture, and buds. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about the reproductive development of animals, and its main contents include: insects, frogs, birds, sexual reproduction, and asexual reproduction. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about bacteria, and its main contents include: overview, morphology, types, structure, reproduction, distribution, application, and expansion. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about plant asexual reproduction, and its main contents include: concept, spore reproduction, vegetative reproduction, tissue culture, and buds. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about the reproductive development of animals, and its main contents include: insects, frogs, birds, sexual reproduction, and asexual reproduction. The summary is comprehensive and meticulous, suitable as review materials.
Quantitative investment
Model
alpha
theoretical type
price
Trend type
trend following, momentum
explain
market equilibrium
Bo silly
MACD
Significance
filtering
conditioning
Reply type
counteri-trend, mean reversion
explain
fluidity
statistical arbitrage
Although contradictory, it takes effect in different time intervals (short-term/long-term)
technical emotion
Fundamentals
value type
carry trade
Quantitative long and short QLS
growth type
sentimentbased strategy
quality type
safety
leverage
diversity of revenue source
volatility of revenue
management quality
fraud risk
mood
plan the details
predicted target
direction
amplitude
Model settings
conditioner
modifying
secondary
Operating frequency
implement
investment period
high frequency
short term
medium term
long
Betting structure
Directionality (single)
Relative (multi-product)
Investment scope
choose
geography
asset class
instrument class
Preference
fluidity
quality data
Systematic model prediction available
Hybrid
Linear
nonlinear
conditional
rotation
machine learning
Do not mix
Build your investment portfolio individually
Portfolios are blended
data driven
Advantage
less competition
market behavior discovery
shortcoming
Relevance of data and results
Large amount of calculation
Reliability questionable
step
1. Observe the market environment
How to define?
2. Match historical data to predict trends
How to define similarity?
Looking back in time?
risk
scale control
Way
Hard constraints
punish
risk measurement
portrait
Volatility
Horizontal
discrete dispersion
Limit range
product
Asset Class
portfolio
Leverage ratio
VaR
Kelly formula
Ways to Eliminate Unintentional Risks
theory driven
statistical method
Principal component analysis
Default model
BARRA
alpha model
cost
source
Commissions and taxes
bid-ask spread cost
slippage
market impact
difficult to separate
Model
constant
Linear
Piecewise linear
secondary
square-root law of market impact
concept
Fund
Hedge
Mutual
private equity
trading
Systemmatic
automated
Strategy identification
Strategy backtesting
Executive system
Risk management
Algorithm
large order execution
pre-grogrammed trading instrctions
minimize the cost, market impact and risk
Quantitative
Valuation
Actuarial-P
Derivatives-Q
Risk management-P
Portfolio management-P
quantitative finance
P-quant
Estimation
forecast the distribution of future returns
Markowitz: mean-variance framework
Q-quant
Calibration
present fair value
Build a portfolio
Based on optimization
MPT
Mean Variance Optimization
Target
risk-adjusted return
Sharpe ratio
enter
expected return
historical returns
Stein estimates
alpha model
variance
historical volatility
alpha model
stochastic volatility models
GARCH
Vix
Correlation coefficient matrix
black-litterman
factor combination group
Resampling/MC Simulation
bootstrap
rule based
alpha weighting
Same position weighting
The statistical significance of unequal weighting is questionable
Risks of unequal weighting
Avoid bad data
Same risk weighting
HRP
handcraft
Kelly Criterion
Position Sizing
discrete bet
continuous betting
Multiple product issues